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Volumetric medical image compression using inter‐slice correlation switched prediction approach
Author(s) -
Sharma Urvashi,
Sood Meenakshi,
Puthooran Emjee
Publication year - 2020
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22425
Subject(s) - computer science , pixel , artificial intelligence , encoding (memory) , computer vision , compression ratio , image compression , data compression , wafer , algorithm , image (mathematics) , image processing , automotive engineering , engineering , internal combustion engine , electrical engineering
With the advancement in medical data acquisition and telemedicine systems, image compression has become an important tool for image handling, as the tremendous amount of data generated in medical field needs to be stored and transmitted effectively. Volumetric MRI and CT images comprise a set of image slices that are correlated to each other. The prediction of the pixels in a slice depends not only upon the spatial information of the slice, but also the inter‐slice information to achieve compression. This article proposes an inter‐slice correlation switched predictor (ICSP) with block adaptive arithmetic encoding (BAAE) technique for 3D medical image data. The proposed ICSP exploits both inter‐slice and intra‐slice redundancies from the volumetric images efficiently. Novelty of the proposed technique is in selecting the correlation coefficient threshold (T ϒ ) for switching of ICSP. Resolution independent gradient edge detector (RIGED) at optimal prediction threshold value is proposed for intra‐slice prediction. Use of RIGED, which is modality and resolution independent, brings the novelty and improved performance for 3D prediction of volumetric images. BAAE is employed for encoding of prediction error image to resulting in higher compression efficiency. The proposed technique is also extended for higher bit depth volumetric medical images (16‐bit depth) presenting significant compression gain of 3D images. The performance of the proposed technique is compared with the state‐of‐the art techniques in terms of bits per pixel (BPP) for 8‐bit depth and was found to be 31.21%, 27.55%, 21.89%, and 2.39% better than the JPEG‐2000, CALIC, JPEG‐LS, M‐CALIC, and 3D‐CALIC respectively. The proposed technique is 11.86%, 8.56%, 7.97%, 6.80%, and 4.86% better than the M‐CALIC, 3D CALIC, JPEG‐2000, JPEG‐LS and CALIC respectively for 16‐bit depth image datasets. The average value of compression ratio for 8‐bit and 16‐bit image dataset is obtained as 3.70 and 3.11 respectively by the proposed technique.