Title :
Fat detection algorithm for liver biopsy images
Author :
Sumitpaibul, Pawesuda ; Damrongphithakkul, Anurak ; Watchareeruetai, Ukrit
Author_Institution :
Dept. of Eng. & Technol., King Mongkut´s Inst. of Technol. Ladkrabang, Bangkok, Thailand
Abstract :
This paper presents an image-processing-based method for analyzing the fat proportion in liver biopsy images. Firstly, the proposed method extracts the area of candidate fat blobs, as well as the background area, from the input image. Then the features of each candidate blobs will be computed. Finally a classification technique called k-nearest neighbors is used to classify each candidate blob if it is fat. Experimental results show that the proposed method can detect fat in the liver biopsy images with the accuracy of 97.52%.
Keywords :
diseases; image classification; image segmentation; liver; medical image processing; classification technique; fat blobs; fat detection algorithm; image-processing based method; input imaging; k-nearest neighbors; liver biopsy imaging; Biomedical imaging; Biopsy; Image resolution; Image segmentation; Liver; image processing; k-nearest neighbors; liver biopsy; liver fat detection;
Conference_Titel :
Electrical Engineering Congress (iEECON), 2014 International
Conference_Location :
Chonburi
DOI :
10.1109/iEECON.2014.6925850
