Multi-Trend Structure Descriptor At Micro-Level For Histological Image Retrieval
M.Natarajan1, S.Sathiamoorthy2

1M.Natarajan, Department of Computer and Information Science, Annamalai University, Annamalai Nagar, India.
2S.Sathiamoorthy, Tamil Virtual Academy, Chennai, India.

Manuscript received on 04 August 2019. | Revised Manuscript received on 08 August 2019. | Manuscript published on 30 September 2019. | PP: 7539-7543 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6120098319/19©BEIESP | DOI: 10.35940/ijrte.C6120.098319
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Abstract: Since hospitals are generating and using image data extensively, medical image databases and its size are rising rapidly. This led to difficulties in browsing and managing the huge databases. Therefore, the necessity for the development of efficient content-based medical image retrieval (CBMIR) system arises and is more challenging problem for researchers. In this paper, to alleviate the unbalanced distribution of image representation using multi-trend structure descriptor (MTSD), MTSD is computed at micro level i.e., image is divided into number of sub-images and for each sub-image MTSD is exploited. In similarity measurement, we compared the MTSDs of corresponding sub-images in query and target images than the liner ordered collection of smallest similarity values between the sub-images are considered for retrieval. Experiments revels that computation of proposed feature at micro level retains the localized representation and considering the liner ordered collection of smallest similarity values between the sub-images provides consistency under illumination changes and noise and thus proposed CBMIR achieves better results.
Keywords: Multi-Trend Structure Descriptor, Micro-Level, Manhattan Measure, Local Structure, Trends.

Scope of the Article:
Image Security