Soft Computing based Medical Image Mining: A Survey

Author(s):  
Amjad Khan ◽  
◽  
Zahid Ansari
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.


2003 ◽  
Vol 20 (1) ◽  
pp. 47-56 ◽  
Author(s):  
S B Mehta ◽  
S Chaudhury ◽  
A Bhattacharyya ◽  
Lazar Mathew

Author(s):  
Amol P. Bhagat ◽  
Mohammad Atique

This chapter presents novel approach fuzzy connectedness image segmentation with geometric moments (FCISGM) for digital imaging and communications in medicine (DICOM) image mining. As most of the medical imaging data is exchanged in DICOM format, this chapter focuses on the various methodologies available for DICOM image feature extraction and mining. The comparison of existing medical image mining approaches with the proposed FCISGM approach is provided in this chapter. After carrying out exhaustive results it has been found that proposed FCISGM method gives more precise results and requires minimum number of computations compare to other medical image mining approaches resulting in improved relevant outcomes.


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