NDLI: 2D Brain slice retrieval from Brain MR images using feature point maps

Content Provider	IEEE Xplore Digital Library
Author	Parthasarathi, M. Ansari, M.A. Dutta, M.K. Burget, R. Riha, K.
Copyright Year	2015
Description	Author affiliation: Sch. of Eng., Gautam Buddha Univ., Noida, India (Ansari, M.A.) \|\| Fac. of Electr. Eng., Brno Univ. of Technol., Brno, Czech Republic (Burget, R.; Riha, K.) \|\| Dept. of Electron. & Commun. Eng., Amity Univ., Noida, India (Parthasarathi, M.; Dutta, M.K.)
Abstract	In this paper, we propose a medical image retrieval scheme which could be of immense help to the medical practitioners. The basic idea is to input a 2D Brain Magnetic Resonance (MR) query slice and output visually similar slices pertaining to the MR image database. Query slice pertaining to a new case can be related to slices belonging to previous case histories in the stored database. It would prove to be a useful diagnostic aid for the medical expert and could also serve as a teaching aid for students and researchers in medical field. Similar pathological cases can also aid in treatment apart from diagnosis. The challenge then is to device an algorithm which is fast, accurate and could be run on low power devices that are installed in clinics and hospitals. To this end, we propose a scheme consisting of a) MR image slice representation by only a few visually significant feature points and b) Similarity distance metric used for retrieval taking into account the spatial information of the local structures.
Starting Page	772
Ending Page	775
File Size	402144
Page Count	4
File Format	PDF
e-ISBN	9781479984985
DOI	10.1109/TSP.2015.7296370
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-09
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain Edge maps Brain lesions Image edge detection Image retrieval Brain MR Slice Feature extraction Hausdorff distance Lesions Medical diagnostic imaging Significant feature points
Content Type	Text
Resource Type	Article

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