Segmentation Approaches for Human Metaspread Chromosome Images Using Level Set Methods
The human metaspread images contain the chromosome s f an individual that are imaged during the metaphase stage of c ll division. The chromosomes are the genetic information carriers, any a lteration either in the number of chromosomes or the structure of chromosomes, res ults in a medical condition termed as genetic defects. The genetic defects can be cause of many diseases that are difficult to cure. In order to uncover the gene tic defects the metaspread images are segmented, to count the number of chromoso mes present or to study the structure of the chromosomes. The metaspread images suffer from intensity in homogeneity, because of which it is very difficult to extract the individual chromosomes by using conventional segmentation techniqu es. In this work various segmentation approaches are implemented and studied to segment the chromosomes from the metaspread images. The minimization of region scalable fitting energy for image segmentation proposed by Chumming l i et. al, is effective in segmenting the metaspread images that have intensit y i homogeneity, as this technique uses the local intensity values of the ne arby regions of the objects and find the approximate intensity values along both si de of the contour. The paper compares the level set based segmentation algorithm s based upon their implementation techniques and the segmentation results. The methods have been compared in terms of number of objects segmented, time tak n, complexity, type of segmentation approach. The ADIR dataset of metspread im ges has been taken for the purpose of experimentation.
Trans. Mass Data Anal. Images Signals