CLASSIFICATION OF BREAST CANCER MALIGNANCY USING

CYTOLOGICAL IMAGES OF FINE NEEDLEASPIRATION BIOPSIES

Breast cancer is the most often diagnosed cancer among women aged 40 to 60.

According to the World Health Organization there are 7.6 million deaths worldwide due to cancer each year, out of which 502,000 are caused by breast cancer alone.

Cancers in their early stages are vulnerable to treatment while cancers in their most advanced stages are usually almost impossible to treat.

The most common diagnostic tools are mammography and a fine needle aspiration biopsy (FNA).

Mammography, which is a non-invasive method, is most often used for screening purposes rather than for precise diagnosis.

It allows a physician to find possible locations ofmicrocalcifications and other indicators in breast tissue.

When a suspicious region is found, the patient is sent toa pathologist for a more precise diagnosis. This is whenthe FNA is taken.

A fine needle aspiration biopsy is an invasive method to extract a small sample of the questionable breast tissue that allows the pathologist to describe the type of the cancer in detail.

Using this method pathologists can very adequately describe not only the type of the cancer but also its genealogy and malignancy.

The stage of cancer depends on the malignancy factor that is assigned during an FNA examination. The determination of malignancy is essential when predicting the progression of cancer.

Block diagram of the cell classification system

Segmentation

Feature Extraction

Classification

Cell images

Application of PR Technique in image segmentation

Fuzzy c-means clustering The FCM clustering algorithm assigns a fuzzy

membership value to each data point based on its proximity to the cluster centroids in the feature space.

FCM is a clustering algorithm, but the resulting partition is fuzzy.

The input feature vectors are not assigned exclusively to a single class, but partially to all classes.

If a single class must be chosen, the data point chosen should be in the class with the higher membership grade.

This is called defuzzification and yields a crisp label.

The FCM algorithm assumes that the number of clusters c is known and minimizes the objective function to find the best set of cluster centers.

Fuzzy c- means clusteringThe standard FCM objective function for partitioning xk ,k=1,2,N into cclusters is given by

The parameter m is a weighing exponent on each fuzzy membership anddetermines the amount of fuzziness of the resulting classification.

The FCM objective function is minimized when high membership valuesare assigned to pixels whose intensities are close to the centroidof its particular class, and low membership values are assignedwhen the pixel data are far from the centroid.

The cluster centers are calculated using

Fuzzy c-means Algorithm

Examplex1=(1, 3) x2=(1.5, 3.2) x3=(1.3, 2.8) x4=(3, 1)