Improving Image Segmentation by DFS Algorithm
G. Hemalatha1, S. Mary Vennila2
1Mrs. G. Hemalatha, Assistant Professor & Research Scholar, Department of Computer science, Bhaktavatsalam Memorial College for Women.
2Dr. S. Mary Vennila, Head, Associate Professor & Research Supervisor, Department of Computer science, Presidency College, Chennai.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3505-3510 | Volume-8 Issue-5, January 2020. | Retrieval Number: B3757078219/2020©BEIESP | DOI: 10.35940/ijrte.B3757.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Medical imagining has proven to be a significant field for examining human tissues non-intrusively. One of the subset of Imaging is the Image segmentation where in an image is split into significant regions which being later used for classification and performing analysis. This process is quiet complex as it involves accurately detecting and removing the affected part of the image containing abnormal tissues which are later being used for analysis. Image segmentation employs numerous techniques and approaches. Though there exist several methods and techniques for image segmentation but all of them can’t be implemented on medical images. The existing paper put forwards a complete survey and review concerning the medical image segmentation models, techniques, algorithms along with the challenges faced with the involvement of contrast filtering and large scale image processing perspectives. The technique of Discrete Feature Segmentation (DFS) is adopted for extracting the attributes related to a medical image. For improvising the contrast of an image, the popular method of Histogram equalization is utilized that basically enlarges the dynamic range of intensity. A method is recommended for defining the parameters of the Contrast-Limited Adaptive Histogram Equalization (CLAHE) by utilizing entropy of image. The CLAHE method that projects intensity levels concerning the medical images is backed up by evidence from detection trials and anecdotal evidence. For classifying the diseases in medical image, the prime emphasis is on the FCM (Fuzzy C-Means (FCM) algorithm. Present research paper compares various techniques of image enhancement considering their quality parameters (PSNR, Mean, MSE, Entropy, SN, Variance and RMS).
Keywords: Medical Image Segmentation; Fuzzy C-Means; Histogram Equalization; Graphics Processing Unit; Discrete Wavelet Transform.
Scope of the Article: Computer Graphics, Simulation, and Modelling.