Semi supervised soft label propagation algorithm for CBIR

Janarthanam, Srinivasan; Sukumaran, S.

doi:10.1109/isco.2016.7726897

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…The Proposed technique is based on the Fisher information measure. Bethapudi et al, proposed a detection and identification method of mass structure in digital images [14] which detect malignant tissues in following steps: (1) Thresholding to remove the background information, (2) Apply median filter for random noise removal, (3) Extract the binary image contours [7]. Thereafter, morphological open and close operations to fill the gaps in holes inside the image region.…”

Section: Related Workmentioning

confidence: 99%

Advanced Region Detection Technique to Enhance Accurate Abnormality Isolation of Cancer in Digital Images

Mallika¹,

Janarthanam²,

Aruljoth³

2019

AJCST

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In recent years, extensive research is carried out in computer assisted interpretation carried out for cancer classification. Computer aided Interpretations are involves with pre-processing, contrast enhancement, segmentation, appropriate feature extraction and classification. Though considerable research is carried out in developing contrast enhancement and image segmentation techniques, cancer regions could not be isolated and extracted efficiently. Hence this work focuses on developing efficient image segmentation techniques for isolating the cancer region and also identifying suitable descriptors for describing the cancer region. Hence this work focuses to introduce a simple and easy approach for detection of cancerous tissues in mammals. Detection phase is followed by segmentation of the region in an image. Our approach uses simple image processing techniques such as averaging and thresholding along with a Max-Mean and Least-Variance technique for cancer detection. Experimental results demonstrate the effectiveness of our approach.

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Section: Related Workmentioning

confidence: 99%