Multi-class Multi-label Classification and Detection of Lumbar Intervertebral Disc Degeneration MR Images using Decision Tree Classifiers

Evidence-based medicine decision-making based on computer-aided methods is a new direction in modernhealthcare. Data Mining Techniques in Computer-Aided Diagnosis (CAD) are powerful and widely used toolsfor efficient and automated classification, retrieval, and pattern recognition of medical images. They becomehighly desirable for the healthcare providers because of the massive and increasing volume of intervertebral discdegeneration images. A fast and efficient classification and retrieval system using query images with high degreeof accuracy is vital. The method proposed in this paper for automatic detection and classification of lumbarintervertebral disc degeneration MRI-T2 images makes use of texture-based pattern recognition in data mining.A dataset of 181segmented ROIs, corresponding to 89 normal and 92 degenerated (narrowed) discs at differentvertebral level, was analyzed and textural features (contrast, entropy, and energy) were extracted from each disc-ROI. The extracted features were employed in the design of a pattern recognition system using C4.5 decisiontree classifier. The system achieved a classification accuracy of 93.33% in designing a Multi-class Multi-labelclassification system based on the affected disc position. This work combined with its higher accuracy isconsidered a valuable knowledge for orthopedists in their diagnosis of lumbar intervertebral disc degeneration inT2-weighted Magnetic Resonance sagittal Images and for automated annotation, archiving, and retrieval of theseimages for later on usage.Keywords: Data Mining, Image Processing, Lumbar Intervertebral Disc Degeneration, MRI-T2, Decision Trees,Multi-class Multi-label Classification

Recognition Memory Dysfunction Relates to Hippocampal Subfield Volume: A Study of Cognitively Normal and Mildly Impaired Older Adults.

ObjectivesThe current study examined recognition memory dysfunction and its neuroanatomical substrates in cognitively normal older adults and those diagnosed with mild cognitive impairment (MCI).MethodsParticipants completed the Mnemonic Similarity Task, which provides simultaneous measures of recognition memory and mnemonic discrimination. They also underwent structural neuroimaging to assess volume of medial temporal cortex and hippocampal subfields.ResultsAs expected, individuals diagnosed with MCI had significantly worse recognition memory performance and reduced volume across medial temporal cortex and hippocampal subfields relative to cognitively normal older adults. After controlling for diagnostic group differences, however, recognition memory was significantly related to whole hippocampus volume, and to volume of the dentate gyrus/CA3 subfield in particular. Recognition memory was also related to mnemonic discrimination, a fundamental component of episodic memory that has previously been linked to dentate gyrus/CA3 structure and function.DiscussionResults reveal that hippocampal subfield volume is sensitive to individual differences in recognition memory in older adults independent of clinical diagnosis. This supports the notion that episodic memory declines along a continuum within this age group, not just between diagnostic groups