title: Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism.
abstract: Intensity normalization is an important pre-processing step in the study and analysis of DaTSCAN SPECT imaging. As most automatic supervised image segmentation and classification methods base their assumptions regarding the intensity distributions on a standardized intensity range, intensity normalization takes on a very significant role. In this work, a comparison between different novel intensity normalization methods is presented. These proposed methodologies are based on Gaussian Mixture Model (GMM) image filtering and mean-squared error (MSE) optimization. The GMM-based image filtering method is achieved according to a probability threshold that removes the clusters whose likelihood are negligible in the non-specific regions. The MSE optimization method consists of a linear transformation that is obtained by minimizing the MSE in the non-specific region between the intensity normalized image and the template. The proposed intensity normalization methods are compared to: i) a standard approach based on the specific-to-non-specific binding ratio that is widely used, and ii) a linear approach based on the α-stable distribution. This comparison is performed on a DaTSCAN image database comprising analysis and classification stages for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome (PS) detection. In addition, these proposed methods correct spatially varying artifacts that modulate the intensity of the images. Finally, using the leave-one-out cross-validation technique over these two approaches, the system achieves results up to a 92.91% of accuracy, 94.64% of sensitivity and 92.65 % of specificity, outperforming previous approaches based on a standard and a linear approach, which are used as a reference. The use of advanced intensity normalization techniques, such as the GMM-based image filtering and the MSE optimization improves the diagnosis of PS.
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.
authors.forename: A
authors.initials: A
authors.lastname: Brahim
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.
authors.forename: J
authors.initials: J
authors.lastname: Ramírez
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.
authors.forename: J M
authors.initials: JM
authors.lastname: Górriz
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.
authors.forename: L
authors.initials: L
authors.lastname: Khedher
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.
authors.forename: D
authors.initials: D
authors.lastname: Salas-Gonzalez
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009662:Nortropanes; D020734:Parkinsonian Disorders / Q000000981:diagnostic imaging*; D025341:Principal Component Analysis; D011877:Radionuclide Imaging; D019275:Radiopharmaceuticals
chemical_list: D009662:Nortropanes; D019275:Radiopharmaceuticals; C519528:ioflupane