Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images. (September 2022)

• Record Type: Journal Article
• Authors: Kaplan, Ela; Altunisik, Erman; Ekmekyapar Firat, Yasemin; Datta Barua, Prabal; Dogan, Sengul; Baygin, Mehmet; Burak Demir, Fahrettin; Tuncer, Turker; Palmer, Elizabeth; Tan, Ru-San; Yu, Ping; Soar, Jeffrey; Fujita, Hamido; Rajendra Acharya, U.
• Is Part Of: Computer methods and programs in biomedicine. Volume 224(2022)
• Publication Date: 2022-09
• Subjects: PD image classification -- Nested patch division -- Local binary pattern -- Local phase quantization -- Neighborhood component analysis -- Image classification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107030
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Three MRI datasets were collected to detect PD symptoms. Nested patch division was presented. A hand-modeled classification architecture was proposed. Our architecture yielded over 98.50% classification accuracies for all cases. Our framework outperformed. Abstract: Objective: Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. Methods: Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classification step. A ten-fold cross-validation technique is used to validate the results. (iv) Eight predicted vectors are generated using four selected feature vectors and two classifiers. Finally, iterative majority voting (IMV) is used to attain general classification results. Therefore, this model is named nested patch-PHOG-multiple feature selectors and multiple classifiers-IMV (NP-PHOG-MFSMCIMV). Results: Our presented NP-PHOG-MFSMCIMV model achieved 99.22, 98.70, and 99.53% accuracies for the collected PD stages, PD dementia, and PD symptoms classification datasets, respectively. Significance: The obtained accuracies (over 98% for all states) demonstrated the performance of developed NP-PHOG-MFSMCIMV model in automated PD state classification.