Tumor demarcation of MRI scan using KMCG vector quantization algorithm

Authors:
H. B. Kekre;T. Sarode;S. Gharge;K. Raut
Affiliations:
MPSTME, NMIMS University, Vile parle(w), Mumbai, India;TSEC, Bandra(W), Mumbai, India;SVKM's NMIMS University, Mumbai, India;TSEC, Bandra(W), Mumbai, India
Venue:
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Year:
2011

Citing 5
Cited 0

A review of recent texture segmentation and feature extraction techniques

CVGIP: Image Understanding
Learning Texture Discrimination Masks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Postal Envelope Address Block Location by Fractal-Based Approach

SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Automatic watershed segmentation of randomly textured color images

IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, texture based segmentation algorithms are considered for comparison. The problem with some of these methods is, they need human interaction for accurate and reliable segmentation. Human interaction is in terms of providing some initial knowledge externally for segmentation. This knowledge is in terms of a small amount of labeled data for some or all classes. This is usually time-consuming and expensive. Segmentation based on Gray level co-occurrence matrix gives better result for variance but computational complexity is more. Watershed gives over segmentation. Morphological provides better segmentation but edges can not get eliminated whereas for segmentation using Kekre's Median Codebook Generation (KMCG) algorithm shows proper tumor demarcation by avoiding other part of the image.