Application of ANN in Data Mining of Medical Images

(08/CS/060)

The computer technology has encouraged the researchers to develop software for assisting doctor in making decision without consulting the specialists directing. In most developing countries insufficient of medical specialists has increase the mortality of patients suffered from diseases. The computer technology used to reduce the waiting time to see the specialists. It help in diagnosing and predicting the patient’s condition.

INTRODUCTION

Classification Medical imaging Association rule mining Neural network Image categorization Image mining process

KEYWORDS

Medical imaging provides a fast, non-invasive way to detect diseases, examine organs and measure therapeutic response.

It is vital in diagnosis and treatment of patients.

Medical imaging allow users to analyze single scan and to monitor patient response and disease progression.

MEDICAL IMAGING

The process of classification includes:- Preprocessing Extraction

Data collection and preprocessing:- The existing data in the collection consists of the location of the abnormality , its radius , position , types of tissue and tumour type if exists.

CLASSIFICATION

Preprocessing phase improve the quality of images and make feature extraction more reliable.Technique used: Cropping operation Image enhancement

PREPROCESSING

Figure 4. Pre-processing phase on an example image.

After cropping and enhancing the images features relevant to the classification are extracted from the cleaned images. The extracted features are organized in a database in the form of transactions.The existing features are : Type of tissue Position of tissue

FEATURE EXTRACTION

The four regions of the first division, and then, for each of the areas is further divided in four.

ANN models have been used in image understanding.

The neural network consists of three layers. The no. of input layer is one transaction in the database.

Output layer gives the classification for image. It classifies as normal or abnormal.

NEURAL NETWORK

InputLayer

HiddenLayer

OutputLayer

This rule typically aim at discovering associations b/w items in a transactional database and describe frequent sets of features per category normal and abnormal.

Two process :-• In first step frequent items are discovered.• In second association rules are derived.

ASSOCIATION RULE MINING

The association rule classified the unbalanced data collection that normal cases and abnormal. This divided into benign and malign. We considered 22 mammograms in existingdatabase . From these 18 benign and 4 malign.The abnormal mammograms are split according to tissue type in fatty, fatty-glandular and dense.

Success rates of association rule mining classifier

Categorization of medical image means selecting the appropriate class for given image out of a set of predefined categories.

IMAGE CATEGORIZATION

ImageAcquisitio

n

ImageEnhancemen

t

FeatureExtraction Classification

ClassificationModel

figure: Image categorization process.

Capturing medical image Storing image an the database Starting up processing Generating Report Validating Report

PROCESSING

Processing Steps

Data mining technique more suitable to larger database than the one used for these preliminary tests.

Association rules becomes more accurate with larger mammographic database than in order of 300 images.

This rule approach , image split in more windows that improve the detection by better localization of the cancerous tumour.

ADVANTAGE

The use of neural network application to provide diagnostic and predictive medical opinions is highly promising for the future of online medical advice.

3-d medical information can provide new frontier in medical education in anatomy and physiology.

Research made an impact an computer assisted surgical planing and image guided interventions.

FUTURE SCOPE

Thank you…