This document presents a proposed method for classifying CT liver images to diagnose diseases using a probabilistic neural network (PNN) with a radial basis function kernel and kernel weighted fuzzy clustering. Existing methods have drawbacks like poor accuracy, inability to handle multiple images quickly, and less discriminatory power. The proposed method extracts Haralick texture features from normalized subband coefficient transformed images and inputs them into a PNN classifier trained on reference samples to classify images as normal, benign, or malignant. Kernel weighted fuzzy clustering is also used for segmentation. The method aims to accurately diagnose liver diseases from CT images in a short time through automated classification and segmentation.

1. ANALYSIS OF CT LIVER IMAGES FOR TUMOR DIAGNOSIS
BASED ON SUPERVISED CLASSIFIER AND CLUSTERING
MODEL
Presented by
1. S.Arun - (951811106009)
2. S.Arunachalam Karthick - (951811106010)
3. J.karthekeyan - (951811106034)
4. S.Mohamed Azharudeen - (951811106049)
Guided By
Mr.S.MUTHUKRISHNAN.,M.E, Asso.Prof./ECE

2. • To diagnose CT liver diseases through stage classification
using probabilistic neural network with RBF kernel and
Kernel weighted fuzzy clustering approach based tumor
detection for computed aided diagnosis system.
Objective

3. Thresholding method
K means clustering
Wavelet and Principal component analysis
KNN classifier
Existing Method

4. Difficult to get accurate results
Not applicable for multiple images for Tumor detection in a short time
Poor discriminatory power
less classification accuracy
Drawbacks

5. CT liver image classification and segmentation for diseases diagnosis system
based on,
• PNN with RBF classifier and Kernel weighted fuzzy clustering approach
Proposed Method

6. CT Liver
Image
PNN-RBF
Training
Features
Extraction
KWFCM
Reference
samples
Haralick matrix
formation
Trained
Network
(classifier)
Segmented
results
Mathematical
morphology
Block Diagram
Features
Extraction
Decision
(if abnormal)
NSCT

7. • Energy : It is a measure the homogeneousness of the
image and can be calculated from the normalized COM.
It is a suitable measure for detection of disorder in
texture image.
• Entropy : Entropy gives a measure of complexity of the
image. Complex textures tend to have higher entropy
Where,
p(i , j) is the co occurrence matrix
Haralick Features

8. • Contrast : Measures the local variations and texture of
shadow depth in the gray level co-occurrence matrix.
• Correlation Coefficient : Measures the joint
probability occurrence of the specified pixel pairs.
sum(sum((x- μx)(y-μy)p(x , y)/σxσy))
• Homogeneity : Measures the closeness of the
distribution of elements in the GLCM to the GLCM
diagonal.
sum(sum(p(x , y)/(1 + [x-y])))
Continues…

9. Neural Network Classifier
• The neural network model PNN is used here to act as a classifier with radial basis
function for network activation function.
• The training samples features with assigned target vectors are fed into created
PNN model for supervised training to get network parameters such as node biases
and weighting factors.
• Finally, test image features are simulating with trained network to make decision
of Liver stages like normal or abnormality(benign and malignant)

10. Training Samples

11. • The pnn is trained with reference features set and desired
output using ‘newpnn’ command. Here, target 1 for
normal, 2 for defect case are taken as desired output.
• After the training, updated weighting factor and biases
with other network parameters are stored to simulate
with input features.
• At the classification stage, test image features are
utilized to simulate with trained network model using
‘sim’ command.
• Finally it returns the classified value as 1, 2 or 3 based
on that the decision will be taken as Normal, Benign or
Malignant.
Classification

12. Test Samples: Normal and Abnormal
Normal Benign Malignant

13. Thank you