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Diagnosis of disc bulge and disc desiccation in lumbar MRI using concatenated shape and texture features with random forest classifier
Author(s) -
Sundarsingh Shirly,
Kesavan Ramesh
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.22374
Subject(s) - artificial intelligence , histogram of oriented gradients , histogram , pattern recognition (psychology) , random forest , local binary patterns , computer science , classifier (uml) , computer vision , image (mathematics)
Disc bulge and disc desiccation are the most common abnormalities occurring in the spine, which leads to severe low back pain. Despite computer‐aided automatic abnormality diagnostic imaging systems are available still there is a need for betterment in diagnostic accuracy and in processing time. Image processing with combined imaging features like shape and texture has given better diagnostic ability when compared with processing with individual features. In the present study, the desiccated and bulged Intervertebral Discs (IVDs) are diagnosed automatically by combining shape features extracted using Histogram of Oriented Gradients (HOG) and texture feature extracted using novel Local Sub‐Rhombus Binary Relation Pattern (LS‐RBRP) techniques with Random Forest (RF) classifier. The performance analysis projects that the RF with HOG+LS‐RBRP has an overall better accuracy of 94.7% when compared with HOG (87%) and LS‐RBRP (90.2%) with RF classifier separately in categorizing the normal IVD, disc bulge and disc desiccation in the lumbar spine MRI.