Melanoma Image Segmentation Using Self Organized Feature Maps

Anirudh MunnangiGraduate Student EECS

University of Cincinnatimunnanah@mail.uc.edu

Overview• Outline of Skin Cancer Detection

• Literature Survey

• Motivation

• Problem Formulation

• SOFM Model

• Model Fitting

• Simulation Results

• Conclusion

Pre-processing• Image pre-processing involves

removing artefacts and noise

Segmentation• Segmentation separates the

cancerous area from healthy skin.

Feature Extraction• Feature extraction selects

classification variables.

Classification• Classification process

employees innovative algorithms to make the final judgment.

Skin Cancer Detection

• Image Processing Models:Adaptive thresholdingFuzzy C means approachRegion growing/merging algorithmsOtsu’s method

• Neural Network Models:MLP using back propagationRadial basis networksUsage of binary image for learning phase

Relevant Literature

• Simple, easy to implement algorithm• Quite novel, no reference in literature• Clustering and segmentation relation• Gray-Scale advanced model• Quite good comparative performance• Scope for further training

Motivation for SOFM

• Effective segmentation • Problems if cancerous area is missed• Role in the detection process• Pixels can be color/grayscale• Computational parameters• Scalability in images

Problem Formulation

Inputs

Neuron Field/Map Each Neuron is connected to all the inputs by weights.

Inputs are binary values; generation is discussed in future slides.

SOFM Model

Learning in SOFM

Courtesy of Ali A Minai, Professor, University of Cincinnati

Courtesy of google Images

• All images are of fixed size 150x200.• Each pixel is treated as a Neuron.• Input is a 1x26 vector.• First 8 elements are X coordinate in binary.• Second 8 elements are Y coordinate in binary.• Third 8 elements are grayscale value in binary.• Last two elements are [1,0] or [0,1] for

cancerous/non cancerous pixel.• While testing; the last two elements are [0,0]

Model Fitting

• Inp_Vec (A) = [01001011 01001011 11111111 10]• Inp_Vec (B) = [10000010 10100000 00101000 01]

A255/ Yes

Y75

X75

B40/No X130

Y160

Model Fitting Sample

Evaluation Parameters

Segmentation

AlgorithmAccuracy Sensitivity Specificity Jaccard

indexDice

coefficient

OTSU’sMethod 0.9464 0.7877 0.9643 0.7147 0.8179

FCM 0.9561 0.7960 0.9723 0.7080 0.8121

Proposed SOFM 0.9748 0.8624 0.9857 0.8586 0.9234

Simulation Results-Evaluation Parameters

Simulation Results-Images

Simulation Results- Evaluation Trends

• Better results are generated.• Evaluation parameters converge in a well behaving

way.• Performs well on an entirely novel test data.• Easy to implement algorithm.• Computational time is high. Once training is done,

results are good.• Can result in slow performance when scaled to

higher resolution images.

Conclusion

Thank you !!