Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism

• Published in: PloS one Vol. 10; no. 6; p. e0130274
• Main Authors: Brahim, A, Ramírez, J, Górriz, J M, Khedher, L, Salas-Gonzalez, D
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18-06-2015; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Basal ganglia; Brain diseases; Central nervous system diseases; Classification; Computer science; Data Interpretation, Statistical; Diagnosis; Diagnosis, Computer-Assisted; Dopamine; Fault diagnosis; Female; Filtration; Humans; Image classification; Image filters; Image processing; Image segmentation; Information theory; Linear transformations; Male; Medical imaging; Middle Aged; Movement disorders; Neurosciences; Nortropanes; Optimization; Parkinson's disease; Parkinsonian Disorders - diagnostic imaging; Principal Component Analysis; Principal components analysis; Radionuclide Imaging; Radiopharmaceuticals; Signal processing; Single photon emission computed tomography; Transformation; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0130274

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.