Automated ovarian masses extraction in CT images based on division of image

Authors

  • Amel Hameed Khaleel Dept. of Computer Science, College of Science, University of Basrah

Keywords:

Ovarian mass, CT (Computed Tomography), Denoising, Segmentation, Morphological Operations, Canny technique.

Abstract

Medical image processing is the most challenging field now a days. Processing of CT ovarian  images is one of the part of this field.The goal of this work is to present a method for detection of ovarian mass from Computed Tomography Image and determine location as well as calculation area of it. Preprocessing of the CT image includes image resizing, conversion to gray and enhancement makes it ready for applying the processing phase which applied operations on processed image by histogram and marker controlled watershed segmentation to segment it to a set of segment that collectively cover the entire image ,then apply morphological operations to visualize only parts of masses in CT images. Because of the nature of ovarian CT images which contain left and right lobes, a proposed algorithm to division  the images after extraction the masses into two  images and then apply algorithm to calculate the area of mass in each parts of image. when execute the proposed method, the results obtained are good compared with  calculation area of the mass of the ovarian extracted by the region of interest algorithm (ROI) using cursor. A detailed procedure using Matlab (R2010a) software is written to extract mass region in CT Scan ovarian Image.

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Published

2017-08-14

How to Cite

Hameed Khaleel, A. (2017). Automated ovarian masses extraction in CT images based on division of image. Journal of Al-Qadisiyah for Computer Science and Mathematics, 6(1), 11–27. Retrieved from https://jqcsm.qu.edu.iq/index.php/journalcm/article/view/122

Issue

Section

Math Articles