detection of suspicious lesions by adaptive thresholding based on multiresolution analysis in...

462 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 60, NO. 2, FEBRUARY 2011

Detection of Suspicious Lesions by AdaptiveThresholding Based on Multiresolution

Analysis in MammogramsKai Hu, Xieping Gao, and Fei Li

Abstract—Mammography is the most effective procedure forthe early detection of breast cancer. In this paper, we develop anovel algorithm to detect suspicious lesions in mammograms. Thealgorithm utilizes the combination of adaptive global thresholdingsegmentation and adaptive local thresholding segmentation on amultiresolution representation of the original mammogram. Thealgorithm has been verified with 170 mammograms in the Mam-mographic Image Analysis Society MiniMammographic database.The experimental results show that the detection method has asensitivity of 91.3% at 0.71 false positives per image.

Index Terms—Adaptive thresholding, breast cancer, computer-aided detection, mammography, multiresolution, segment, suspi-cious lesions.

I. INTRODUCTION

B REAST cancer is the most common form of cancer inAmerican women and the second major cause of death

[1]. A report estimated that one in eight women in the U.S. and1 in 13 in Australia develop breast cancer during their lifetime[2]. In order to reduce morbidity and mortality, early detectionof breast cancer is essential. The previous study shows thataccurate early detection can effectively reduce the mortalityrate caused by breast cancer, and mammography is currentlythe best technique for reliable detection of early nonpalpablecurable breast cancer [3]. However, the appearances of breastcancers are very subtle and unstable in their early stages.Therefore, doctors and radiologists can miss the abnormalityeasily if they only diagnose by experience. The computer-aided detection technology can help doctors and radiologists ingetting a more reliable and effective diagnosis, since it checksthe mammograms as the “second reader,” thus giving to doctorsand radiologists a favorable advice.

Usually, a detection algorithm consists of two main steps:The first step is to detect suspicious lesions with segmentation

Manuscript received November 18, 2009; revised April 3, 2010; acceptedApril 19, 2010. Date of publication June 10, 2010; date of current versionJanuary 7, 2011. This work was supported in part by the National Natural Sci-ence Foundation of China under Grant 60375021 and in part by the Foundationof Hunan Educational Committee under Grant 09B104. The Associate Editorcoordinating the review process for this paper was Dr. Domenico Grimaldi.

The authors are with the College of Information Engineering and the KeyLaboratory of Intelligent Computing and Information Processing of Ministry ofEducation, Xiangtan University, Xiangtan 411105, China (e-mail: [email protected]; [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TIM.2010.2051060

or filter technologies; the second one is to reduce false positivesand then obtain final results.

According to the representations of breast cancer in mam-mograms, lesions can be classified as microcalcification andspace occupying. We only discuss the latter in this paper. Space-occupying lesions are divided into three cases: masses, archi-tectural distortion (ARCH), and asymmetry (ASYM). Amongthem, masses and ARCH are the typical signal characteris-tics of breast cancer. According to the shape and boundarycharacteristics of masses, it can be further divided into spicu-lated masses (SPIC), circumscribed masses (CIRC), and othermasses (MISC) [4]. Fig. 1 shows the classification of lesions,and Fig. 2 shows the typical examples of real space-occupyinglesions (excluding the asymmetric structure). In mammogra-phy, there are three main lesion features: texture, shape, andgray level. In recent years, several lesion-feature-based schemesfor mass detection and segmentation have been developed. Forexample, SPIC and ARCH lesions can be mainly characterizedby lines radiating from the central nucleus to their margins withoriented textural patterns. Thus, researchers have proposed a se-ries of texture-feature-based detection algorithms (see, e.g., [1],[2], and [5]–[8]). Since CIRC lesions are mainly characterizedby shape features, some algorithms [9]–[12] were proposedand achieved good detection results. For MISC and ASYMlesions, and other lesions that are mainly characterized by gray-level features, such as brightness and gray value, gray-level-feature-based detection algorithms [10], [13]–[17] can obtainmore comprehensive results and are effective in mammographicmass detection, particularly the adaptive thresholding detectionalgorithms of Zhang and Desai [15] and Kom et al. [17].

Wavelet-transform-based methods offer a natural frameworkfor multiscale image representations that can be separately ana-lyzed [15], [18], [19]. Zhang and Desai [15] proposed a wavelettransform to input images, which made gray-level Probabil-ity Density Function (PDF) of the low-frequency subimagesapproach to Gaussian distribution. Then, they performed 1-Dwavelet-based analysis to the PDF and adaptively selectedproper thresholds for segmentation by searching for the localminima of the 1-D wavelet transformed PDF. This method issimple, fast, and effective for segmenting tumors in mammo-grams. However, the method is not very effective when thetarget and the background regions demonstrate little differencein gray-level values. Compared to the aforementioned globalthresholding method, Kom et al. [17] presented an approachto segment the suspicious mass regions by a local adaptive

0018-9456/$26.00 © 2010 IEEE

HU et al.: DETECTION OF LESIONS BY ADAPTIVE THRESHOLDING BASED ON ANALYSIS IN MAMMOGRAMS 463

Fig. 1. Classification of lesions.

Fig. 2. Typical examples of different types of lesions. (a) CIRC. (b) SPIC. (c) ARCH. (d) MISC.

thresholding technique after the mammograms are enhancedwith a linear transformation filter. For each pixel of the image,a threshold is computed according to the neighboring windowsaround the pixel. Next, a decision is made to classify the pixelwhether it belongs to a suspicious lesion or a normal regionby the threshold. Having been tested in 61 mammograms,this algorithm obtained a sensitivity of 95.91%. The area Az

under the receiver operating characteristic (ROC) curve was0.946 (with enhancement) and 0.938 (without enhancement).From the experimental results, we can see that this algorithmworks well in mammographic mass detection. At the same time,experiments show that the algorithm has a shortage: It did notconsider the case where a mass contains the small window, thecenter region of a suspicious lesion is not detected, and it givesan empty area in the segmentation result.

From the experimental results, we can see that the aforemen-tioned detection results for lesions are mainly characterized bya single feature. In other words, the algorithm can obtain gooddetection results on one type of lesions, but it may generateunreasonable detection results on other types of lesions. In thispaper, we will present a novel detection algorithm, which com-bines the gray-level feature and the shape feature to generatemore reliable and reasonable detection results, since the gray-level feature and shape feature are two of the most commoncharacteristics of all types of lesions. The proposed algorithmnot only improves the detection results effectively based on aprevious adaptive thresholding technique but also extends thedetection process from a single resolution to multiresolution.The global and local thresholds are chosen adaptively withoutartificial. We have used the mammograms obtained from theMammographic Image Analysis Society (MIAS) MiniMam-mographic database to test the proposed algorithm. The ex-perimental results show that the presented algorithm works

effectively for mammographic image segmentation and detec-tion. The rest of this paper is organized as follows. Section IIis a brief review of the adaptive thresholding methods basedon histogram and window. In Section III, the proposed detec-tion algorithm is described in detail. Section IV gives severaldetection examples and discusses the experimental results, andSection V concludes our work.

II. REVIEW

A. Histogram-Based Adaptive Thresholding Method

According to Zhang and Desai [15], after the mammogramsare wavelet transformed, the gray-level distribution of the tar-get and the background regions of the images approaches toGaussian distribution. Moreover, the target has higher graylevel than the background. That is, if pb(x) and pt(x) denotethe PDFs of the background and the target, respectively, then

pb(x) =1√

2πσ1

exp{− (x − μ1)2

2σ21

}

pt(x) =1√

2πσ2

exp{− (x − μ2)2

2σ22

}, μ2 > μ1 (1)

where x is a pixel value, μ1 and μ2 are the means of thebackground and the target of image, and σ1 and σ2 are thestandard deviations of the background and the target of image,respectively.

Let pI(x) be the PDF of image I , and let p(B) and p(T ) bethe a priori probabilities of the background and the target ofimage I , respectively. We have

pI(x) = p(B)pb(x) + p(T )pt(x). (2)


Fig. 3. Relationship among pI(x), pb(x), and pt(x).

Fig. 4. Bayes threshold λ1 and the proposed candidate threshold λ3 areindicated.

The relationship among pI(x), pb(x), and pt(x) is shownin Fig. 3.

The Bayes threshold λ1 [15] is the intersection of two solidlines that satisfy p(B)pb(λ1) = p(T )pt(λ1). In fact, segmenta-tion according to threshold λ1 is a process of classifying pixels.Let binary image R be the segmentation result; then

R(i, j) ={

0, SI(i, j) < λ1

1, SI(i, j) ≥ λ1(3)

where (i, j) denote the pixel coordinates and SI(i, j) denotesthe pixel value of (i, j). Usually, the Bayes threshold λ1 cannotbe calculated because pb(x), pt(x), and the a priori probabilityof each class are unknown. Assume that λ2 is the minimumvalue in pI(x). Zhang and Desai have proved that, when theoverlap between pb(x) and pt(x) is not significant, λ2 is oftenclose to λ1. Hence, it is reasonable to carry out segmentationaccording to λ2.

However, when pb(x) and pt(x) are not ideal and the overlapbetween them is large, the algorithm of Zhang and Desai doesnot work anymore. For example, in the case shown in the topimage of Fig. 4, we cannot determine λ2 by selecting the localminima because λ2 is not the minima on the right of μ1, i.e.,the global maximum in pI(x).

In this case, we select the threshold from the derivative PDFcurve of pI(x), as described in Section III. The top image ofFig. 4 shows the PDF curve of pI(x), and the bottom imageof Fig. 4 shows the absolute value of derivation of pI(x), i.e.,|p′I(x)|, and λ3 is the local minima of |p′I(x)|. Therefore, we

Fig. 5. Windows for adaptive thresholding.

can obtain the segmentation result effectively in this case byusing threshold λ3.

B. Window-Based Adaptive Thresholding Method

Local segmentation is expected to give more precise resultssince the global segmentation finds a coarse localization of thesuspicious lesions. In [17], for each pixel SI(i, j), a decision ismade to classify it into a potential suspicious lesion pixel or anormal pixel by the following rule.

If SI(i, j) ≥ TH(i, j) and SIdif ≥ MvoisiP , then SI(i, j)belongs to the suspicious area; else, SI(i, j) belongs to thenormal area. In this rule, TH(i, j) is an adaptive thresholdvalue calculated by

TH(i, j) = MvoisiP + γ · SIdif

with SIdif = SImax(i, j) − SImin(i, j). (4)

MvoisiP is an average of pixel intensity in a small windowaround the pixel SI(i, j); SImax(i, j) and SImin(i, j) are themaximum and minimum intensity values in the large windowas shown in Fig. 5. γ is a thresholding bias coefficient. Its valueranges from zero to one.

From (4) and the experimental results, we can see that thealgorithm does not consider the case that a mass contains thesmall window. If pixel SI(i, j) in the circled bright regionis large and close to MvoisiP , it should be segmented as alesion pixel. However, the center area of the bright region isnot detected by using (4), as shown in Fig. 6(b) and (d). Forthe cases shown in Fig. 6(a) and (c), the suspicious regionpixels’ thresholds should be calculated in another way. We willpresent a new algorithm to select the threshold for each pixel inSection III.

III. DETECTION METHOD

A. Detection Algorithm of Suspicious Lesions by AdaptiveThresholding Based on Multiresolution Analysis

The adaptive thresholding detection algorithm is proved tobe an effective method to detect lesions in mammogram [15],[17]. In these algorithms, the key step is to segment a suspiciousregion by a threshold. Thus, how to select a threshold is themost important issue.

The adaptive histogram thresholding technique is based onthe gray-level values [15]. The threshold selected by the PDFcurve is a global threshold for the whole image. For obviouslesion regions, the gray-level values have a global superiorityin the whole images and can be easily segmented as suspicious


Fig. 6. Examples of empty area in the segmentation results by the adaptive thresholding segmentation of Kom et al. [17]. [(a) and (c)] Original images of mdb028and mdb134 in mini-MIAS, respectively. The circled region contains the lesion. [(b) and (d)] Segmentation results of mdb028 and mdb134 by the algorithm ofKom et al., respectively.

Fig. 7. Segmentation results by histogram-based adaptive thresholding segmentation. [(a), (c), (e), and (g)] Original images of mdb025, mdb021, mdb155, andmdb264 in mini-MIAS, respectively. The circled region contains the lesion. [(b), (d), (f), and (h)] Segmentations of mdb025, mdb021, mdb155, and mdb264,respectively.

area. This method is simple, fast, and effective for segmentingtumors in mammograms. However, one of the main difficultiesin suspicious mass segmentation is that mammographic massesare often overlapped with dense breast tissues, which mayhave higher density than the masses. Therefore, it is difficultto directly segment the region of lesions with high accuracyin the mammograms by global gray-level thresholding. Fig. 7shows the histogram- based adaptive thresholding segmentationresults. From the case in Fig. 7(c) and (d), we can see that thealgorithm failed to produce valuable results.

The window-based adaptive thresholding technique adap-tively selects threshold for each pixel in definite neighboringwindows [17]. For obvious lesion regions, the pixels can beeasily segmented since they have a superiority of gray-levelvalues in their neighboring windows. The algorithm in [17]can effectively detect suspicious lesions in a local area inmammograms. However, this algorithm also segments normaltissues into lesions if these normal tissues have a superiority ofgray-level values in the neighboring windows. Fig. 8 shows thesegmentation results by window-based adaptive thresholding

segmentation. From Fig. 8(g) and (h), we can see that thealgorithm did not generate the accurate segmentation result forthe lesion.

In this paper, we propose to detect suspicious lesions byadaptive thresholding based on multiresolution in mammo-grams. By performing two times Daubechies wavelet (DB10)transforms, we can obtain low-frequency subimages at differentresolutions. Let I0 be the original mammogram, which has thefinest resolution; I1 and I2 are the subimages of lower resolu-tions of the original image. I0, I1, and I2 constitute a multireso-lution representation of the original mammogram. Detection isperformed from the coarsest resolution to the finest resolutionusing adaptive thresholding techniques. There is a fundamentaldifference between our algorithm and other approaches, whichis also the novelty of our algorithm: We use a combination oftwo thresholding segmentations, i.e., a coarse segmentation anda fine segmentation, to segment suspicious lesions in multiscaleimages. We first use the coarse segmentation to get a roughrepresentation of the localization of suspicious lesions and thenuse the fine segmentation to improve the rough representation


Fig. 8. Segmentation results by window-based adaptive thresholding segmentation. [(a), (c), (e), and (g)] Original images of mdb025, mdb021, mdb155, andmdb264 in mini-MIAS, respectively. The circled region contains the lesion. [(b), (d), (f), and (h)] Segmentations of mdb025, mdb021, mdb155, and mdb264,respectively.

to generate more precise segmentation results. Our algorithmavoids the deficiencies of the histogram-based and the window-based thresholding algorithms and improves the segmentationaccuracy effectively. Fig. 9 shows the diagram of the proposeddetection of suspicious lesions by our algorithm.

We use a histogram-based adaptive thresholding technique toimplement the coarse segmentation. A global segmentation re-sult is obtained by applying this technique to I2. Our algorithmovercomes the shortcomings of existing algorithms described inSection II-A. In the top image of Fig. 4, we select the thresholdfrom the derivative PDF curve of pI(x). Thus, (5) is employedto select the threshold for the coarse segmentation.

λ1 =

{λ2, λ2 is availableλ3, λ2 is not available, λ3 is availableμ1, λ2, λ3 are not available.

(5)

Note that μ1 is not an approximation to the Bayes threshold.We do not lose the target regions at the current segmentation byselecting μ1. When the threshold is determined, segmentationis implemented using (3). When the first global thresholdsegmentation is finished, it gives a rough representation of thelocalization of suspicious lesions in mammograms.

After the coarse segmentation, we use a window-based adap-tive thresholding technique to implement the fine segmentation.In our study, the small window size ranges from 5 × 5 pixels to15 × 15 pixels, and 128 × 128 pixels to 256 × 256 pixels for thelarge window. For each pixel in the breast area, a threshold iscomputed in order to identify it as a suspicious lesion pixel or anormal tissue pixel by local windows. To improve the existingalgorithm in [17] for selecting a threshold for each pixel, wepresent a new algorithm as follows.

If MvoisiP > α · SIdif and (|MvoisiP − SI(i, j)|/MvoisiP ) < 1 − α, then SI(i, j) is likely to be a pixel in a

potential suspicious mass, and TH(i, j) is calculated as

TH(i, j) ={

α · MvoisiP, if MvoisiP > SI(i, j)MvoisiP, otherwise

(6)

else

TH(i, j) =MvoisiP + γ · SIdif

with SIdif =SImax(i, j) − SImin(i, j) (7)

where α and γ are thresholding bias coefficients, both oftheir values ranging from zero to one. In particular, α is thekey parameter. We use it as the decision threshold to get thefree-response ROC (FROC) curve to analyze the experimentalresults. After many tests, with several values of γ chosenempirically according to the pixel values of testing images,we obtain good result with γ = 0.5. The fine segmentation isa local segmentation since the threshold is just for each pixelSI(i, j) [17]. The experimental results show that, through thefine segmentation, we get more precise segmentation results ofsuspicious lesions.

B. Enhancement and Refinement at Finer Resolution

Let B2 and B′1 be the segmentation results after the coarse

segmentation and the fine segmentation, respectively. In orderto match the image into a finer resolution, B2 and B′

1 aremapped to B1 and B0, respectively. The process is implementedby (8). Each pixel in Bj corresponds to a 2 × 2 matrix inBj+1, i.e.,

Bj−1(2m + l, 2n + k) = Bj(m,n), l, k ∈ {−1, 0} . (8)


Fig. 9. Diagram of the proposed detection method.

Meanwhile, by enhancing I1 with a morphological filter, weobtain M1. The details about morphological filter technique canbe found in [10].

After the enhancement, the image A1 is obtained bythe convolution of M1 and B1, as shown in the followingequation:

A1 = Cov(B1,M1). (9)

IV. EXPERIMENTAL RESULTS AND DISCUSSIONS

The data used in our experiments are obtained from the mini-MIAS database of mammograms [4]. The same collection hasalso been used in other studies, such as automatic mammo-gram classification, mass segmentation, and microcalcificationdetection. All images are digitized at the resolution of 1024 ×1024 pixels and 8-bit accuracy (gray level). The proposedalgorithm was implemented in a MATLAB environment


Fig. 10. FROC analysis for the proposed detection system.

on a PC (Intel Pentium IV, 3.0-GHz CPU, and 512-MBRAM). It has been verified with 170 mammograms in themini-MIAS database. The testing images include 81 normalimages and all the 89 real space-occupying lesion images, with92 lesions in total.1 Among these mammograms, there are22 images of CIRC and 24 lesions, 19 images of SPIC and19 lesions, 19 images of ARCH and 19 lesions, 15 images ofASYM and 15 lesions, and 14 images of MISC and 15 lesions.To evaluate the computer-aided diagnosis results, we adoptedthe following overlapping criteria in [10], [13]: A computer-aided finding is considered as a correct result if its area isoverlapped by at least 50% of a true lesion. The detection resultsare evaluated by terms of sensitivity and the number of falsepositives per image (FP/I).

Fig. 10 shows the FROC curve of the proposed detectionmethod. In the FROC curve, x-axis represents the averagenumber of FP/I, and y-axis represents the true positive fraction(TPF), also known as sensitivity. ROC analysis is based onstatistical decision. The distinction between positive and neg-ative in a segmentation result is sometimes artificial, because itis not always easy to confirm that a mammogram has a massor not [17]. In this paper, we used α described previously asthe decision threshold to count the number of detected lesionsby the proposed algorithm. The FROC curves were gener-ated by setting different discriminating thresholds. Among the170 mammograms, the proposed detection system obtained asensitivity of 91.3% at 0.71 FP/I. Table I shows the detectionresults for different lesions.

Getting an accurate suspected region is a crucial issue be-cause geometric features are extracted based on suspectedregions, and these features are very important for further truelesion detection. For the CIRC lesions, their shapes are usuallyclose to round or oval, and the morphological filter used by theproposed algorithm can enhance the segmentation capabilityfor these objects. Therefore, the proposed algorithm obtaineda good detection result, i.e., 95.8%, for CIRC. In MISC shapemammograms, the centers of most of the lesions have highergray-level values than background regions. The shapes of

1As the lesion’s locations in mdb059 are not provided by MIAS, mdb059 isnot used in the experiment.

TABLE IDETECTION RESULTS FOR DIFFERENT LESIONS

ASYM usually do not have a significant feature of texture orshape; gray level is a more representative feature. Since the twoshapes of lesions have relatively large pixel values and the veryobvious gray-level features, the proposed detection algorithmbased on the adaptive gray-level threshold worked very well andobtained a robust performance. The corresponding sensitivitiesfor both MISC and ASYM shapes reach 93.3%. Texture isthe most used feature for SPIC and ARCH, as there are notexture features taken into account by the proposed algorithm.The sensitivities for SPIC and ARCH are 78.9% and 94.7%,respectively. Although not all the detection results of SPIC andARCH are obtained in high accuracy, they are still more reliableand reasonable than those obtained by some of other methodsin the literature, particularly for ARCH.

As expected in the experiment, the wavelet transform on theoriginal mammograms removed the singularities and generatedthe lesion gray-scale information. Subsequently, the wavelettransform on the histograms (PDF curves) removed the fluc-tuations. Hence, the global local minima can be found as theadaptive global threshold to implement the coarse segmenta-tion. In the meantime, the morphological filter on the trans-formed images not only removed the background noise andthe structure noise inside the suspected mass pattern but alsoenhanced the gray-level feature and shape feature of lesions.Finally, after a convolution between the coarse segmentationand the morphological enhancement filtered gray-level image,it is easier to select the adaptive local threshold to perform thefine segmentation. Experimental results show that the proposedalgorithm performs effectively for the lesion detection in mam-mograms.

Taking into account that Cao et al. used the same databaseas our algorithm to test their algorithm in [22], we provideherein a detailed comparison between our algorithm and thatof Cao et al. In [22], Cao et al. proposed a detection methodwhich has been verified with 60 mammograms in the mini-MIAS database and achieved a TPF of 90.7% with averageof 2.57 FP/I. Among these mammograms, six contain normalbreast tissue only, and 54 have at least one lesion surrounded byglandular and dense glandular breast tissues [22]. For furthercomparison, we have tested the proposed algorithm on thesemammograms. However, we cannot give one-to-one compari-son directly, since Cao et al. [22] did not indicate the numberof normal mammograms, even though we have tested dozens ofsix normal images together with other 54 images as describedearlier. In addition, we have obtained a sensitivity of 94.4% atthe mean false positive value of one per image. The best false


TABLE IIDETECTION RESULTS OF THE NUMBER OF SUSPICIOUS REGIONS PER IMAGE FOR THE PROPOSED METHOD,

THE METHOD OF CAO et al. [22], AND THE GROUND TRUTH PROVIDED BY MIAS

Fig. 11. Detection results of CIRC lesions. From top to bottom are the cases of mdb012, mdb028, and mdb069, respectively. (a) Original mammograms. Thecircled region contains the lesion. (b) Images after coarse segmentation. (c) Convoluted images. (d) Images after fine segmentation.


Fig. 12. Detection results of SIPC lesions. From top to bottom are the cases of mdb181, mdb202, and mdb204, respectively. (a) Original mammograms. Thecircled region contains the lesion. (b) Images after coarse segmentation. (c) Convoluted images. (d) Images after fine segmentation.

Fig. 13. Detection results of ARCH lesions. From top to bottom are the cases of mdb117, mdb121, and mdb152, respectively. (a) Original mammograms. Thecircled region contains the lesion. (b) Images after coarse segmentation. (c) Convoluted images. (d) Images after fine segmentation.

positive is 0.83 per image, and the worst is 1.42. The detectionresults are illustrated in Table II.

As for [17] and other similar references, we cannot obtain thesame images, as they used their own clinical images to test their

algorithms. Thus, we cannot provide an integrated comparisonbetween the proposed algorithm and those existing algorithms.

Figs. 11–15 show the examples of detection steps of CIRC,SPIC, ARCH, ASYM, and MISC lesions, respectively.


Fig. 14. Detection results of ASYM lesions. From top to bottom are the cases of mdb072, mdb083, and mdb097, respectively. (a) Original mammograms. Thecircled region contains the lesion. (b) Images after coarse segmentation. (c) Convoluted images. (d) Images after fine segmentation.

Fig. 15. Detection results of MISC lesions. From top to bottom are the cases of mdb032, mdb134, and mdb271, respectively. (a) Original mammograms. Thecircled region contains the lesion. (b) Images after coarse segmentation. (c) Convoluted images. (d) Images after fine segmentation.

V. CONCLUSION

We have presented a novel algorithm for the detection ofsuspicious lesions in mammography. Wavelet transforms are

used in the proposed method, and a combination of adaptiveglobal thresholding segmentation and adaptive local thresh-olding segmentation is used to segment the multiresolution


subimages of the original mammogram. First, a histogram-based adaptive global thresholding algorithm is used to segmentthe image to get the coarse segmentation. An approach forchoosing the threshold adaptively by looking for the globalminima of the PDF curves of a wavelet-transformed image isproposed to implement the global segmentation. Second, after aconvolution between the coarse segmentation and the morpho-logical enhancement filtered gray-level image, a window-basedadaptive local thresholding method is performed to obtain thefine segmentation. Experimental results using the mini-MIASimage database have shown that the proposed detection systemis capable of detecting suspicious lesions of different types atlow false positive rates.

Furthermore, the detection results for some types of lesionsmainly characterized by texture feature may be improved ifother combinations of lesion features are taken into accountin the presented algorithm. How to combine all three lesionfeatures for all types of lesions is the next objective of our futureresearch.

ACKNOWLEDGMENT

The authors would like to thank the anonymous reviewersfor their insightful comments, which have contributed greatlyto improve the content of this paper.

REFERENCES

[1] S. Liu, C. F. Babbs, and E. J. Delp, “Multiresolution detection of spic-ulated lesions in digital mammograms,” IEEE Trans. Image Process.,vol. 10, no. 6, pp. 874–884, Jun. 2001.

[2] K. Bovis and S. Singh, “Detection of masses in mammograms usingtexture features,” in Proc. 15th Int. Conf. Pattern Recog., 2000, vol. 2,pp. 267–270.

[3] G. Cardenosa, “Mammography: An overview,” in Proc. 3rd Int. WorkshopDigital Mammography, Chicago, IL, Jun. 9–12, 1996, pp. 3–10.

[4] J. Suckling, S. Astley, D. Betal, N. Cerneaz, D. R. Dance,S.-L. Kok, J. Parker, I. Ricketts, J. Savage, E. Stamatakis, andP. Taylor, Mammographic Image Analysis Society MiniMammo-graphic Database, 2005. [Online]. Available: http://peipa.essex.ac.uk/ipa/pix/mias/

[5] M. Zhang, M. L. Giger, C. J. Vyborny, and K. Doi, “Mammographictexture analysis for the detection of spiculated lesions,” in Proc. 3rd Int.Workshop Digital Mammography, K. Doi, M. L. Giger, R. M. Nishikawaand R. A. Schmidt, Eds., Chicago, IL, Jun. 9–12, 1996, pp. 347–350.

[6] F. J. Ayres and R. M. Rangayyan, “Characterization of architecturaldistortion in mammograms,” IEEE Eng. Med. Biol. Mag., vol. 24, no. 1,pp. 59–67, Jan./Feb. 2005.

[7] N. Karssemeijer and G. M. te Brake, “Detection of stellate distortionsin mammogram,” IEEE Trans. Med. Imag., vol. 15, no. 1, pp. 611–619,Oct. 1996.

[8] D. Guliato, R. M. Rangayyan, J. D. Carvalho, and S. A. Santiago,“Polygonal modeling of contours of breast tumors with the preserva-tion of spicules,” IEEE Trans. Biomed. Eng., vol. 55, no. 1, pp. 14–20,Jan. 2008.

[9] H. Kobatake, M. Murakami, H. Takeo, and S. Nawano, “Computerizeddetection of malignant tumors on digital mammograms,” IEEE Trans.Med. Imag., vol. 18, no. 5, pp. 369–378, May 1999.

[10] H. Li, Y. Wang, K. J. Ray Liu, S.-C. B. Lo, and M. T. Freedman, “Com-puterized radiographic mass detection—Part I: Lesion site selection bymorphological enhancement and contextual segmentation,” IEEE Trans.Med. Imag., vol. 20, no. 4, pp. 289–301, Apr. 2001.

[11] B. R. Groshong and W. P. Kegelmeyer, Evaluation of a Hough Trans-form Method for Circumscribed Lesion Detection, K. Doi, M. L. Giger,R. M. Nishikawa, and R. A. Schmidt, Eds. Amsterdam, TheNetherlands: Elsevier, 1996, pp. 361–366.

[12] A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli,“Mammographic images enhancement and denoising for breast cancerdetection using dyadic wavelet processing,” IEEE Trans. Instrum. Meas.,vol. 57, no. 7, pp. 1422–1430, Jul. 2008.

[13] G. M. te Brake and N. Karssemeijer, “Segmentation of suspicious den-sities in digital mammograms,” Med. Phys., vol. 28, no. 2, pp. 259–266,Feb. 2001.

[14] S. Singh and K. Bovis, “An evaluation of contrast enhancement techniquesfor mammographic breast masses,” IEEE Trans. Inf. Technol. Biomed.,vol. 9, no. 1, pp. 109–119, Mar. 2005.

[15] X. P. Zhang and M. D. Desai, “Segmentation of bright targets usingwavelets and adaptive thresholding,” IEEE Trans. Image Process., vol. 10,no. 7, pp. 1020–1030, Jul. 2001.

[16] X. P. Zhang, “Multiscale tumor detection and segmentation in mammo-grams,” in Proc. IEEE Int. Symp. Biomed. Imag., Jul. 2002, pp. 213–216.

[17] G. Kom, A. Tiedeu, and M. Kom, “Automated detection of massesin mammograms by local adaptive thresholding,” Comput. Biol. Med.,vol. 37, no. 1, pp. 37–48, Jan. 2007.

[18] S. Mallat, “A theory for multiresolution signal decomposition: Thewavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11,no. 7, pp. 674–693, Jul. 1989.

[19] I. Daubechies, Ten Lectures on Wavelets. Philadelphia, PA: SIAM, 1992.[20] F. Fauci, S. Bagnasco, R. Bellotti, D. Cascio, C. Cheran, F. De Carlo,

G. De Nunzio, M. E. Fantacci, G. Forni, A. Lauria, E. Lopez Torres,R. Magro, G. L. Masala, P. Oliva, M. Quarta, G. Raso, A. Retico, andS. Tangaro, “Mammogram segmentation by contour searching and masslesions classification with neural network,” IEEE Trans. Nucl. Sci.,vol. 53, no. 5, pp. 2827–2833, Oct. 2006.

[21] N. H. Eltonsy, G. D. Tourassi, and A. S. Elmaghraby, “A concentricmorphology model for the detection of masses in mammography,” IEEETrans. Med. Imag., vol. 26, no. 6, pp. 880–889, Jun. 2007.

[22] A. Z. Cao, Q. Song, and X. L. Yang, “Robust information clus-tering incorporating spatial information for breast mass detection indigitized mammograms,” Comput. Vis. Image Understand., vol. 109,no. 1, pp. 86–96, Jan. 2008.

Kai Hu was born in 1984. He received the B.S.degree in computer science and technology fromXiangtan University, Xiangtan, China, in 2007,where he is currently working toward the Ph.D.degree in computational mathematics.

His research interests focus on wavelet analysisand image processing.

Xieping Gao was born in 1965. He received theB.S. and M.S. degrees from Xiangtan University,Xiangtan, China, in 1985 and 1988, respectively, andthe Ph.D. degree from Hunan University, Changsha,China, in 2003.

He was a Visiting Scholar with the National KeyLaboratory of Intelligent Technology and Systems,Tsinghua University, Beijing, China, from 1995 to1996, and with the School of Electrical and Elec-tronic Engineering, Nanyang Technological Univer-sity, Singapore, from 2002 to 2003. He is currently

a Professor with the College of Information Engineering, Xiangtan University.He is a regular reviewer for several journals, and he has been a member ofthe technical committees of several scientific conferences. He has authored andcoauthored over 80 journal papers, conference papers, and book chapters. Hiscurrent research interests are in the areas of wavelet analysis, neural networks,evolution computation, and image processing.

Fei Li was born in 1980. He received the B.S. andM.S. degrees from Xiangtan University, Xiangtan,China, in 2002 and 2006, respectively.

He is currently with the College of InformationEngineering and the Key Laboratory of IntelligentComputing and Information Processing of Ministryof Education, Xiangtan University. His recent re-search interest is image processing.

detection of suspicious lesions by adaptive thresholding based on multiresolution analysis in...

Documents