Detection and Demarcation of Tumor using Vector Quantization in MRI images

TL;DR

This paper introduces a vector quantization-based segmentation method using the Linde Buzo-Gray algorithm for MRI images to improve tumor detection accuracy in breast cancer diagnosis.

Contribution

The paper presents a novel segmentation approach using vector quantization and LBG algorithm, avoiding over- or under-segmentation in MRI tumor detection.

Findings

Effective segmentation without over/under-segmentation

Comparison shows competitive results with watershed and entropy methods

Method suitable for preprocessing in breast cancer detection

Abstract

Segmenting a MRI images into homogeneous texture regions representing disparate tissue types is often a useful preprocessing step in the computer-assisted detection of breast cancer. That is why we proposed new algorithm to detect cancer in mammogram breast cancer images. In this paper we proposed segmentation using vector quantization technique. Here we used Linde Buzo-Gray algorithm (LBG) for segmentation of MRI images. Initially a codebook of size 128 was generated for MRI images. These code vectors were further clustered in 8 clusters using same LBG algorithm. These 8 images were displayed as a result. This approach does not leads to over segmentation or under segmentation. For the comparison purpose we displayed results of watershed segmentation and Entropy using Gray Level Co-occurrence Matrix along with this method.