title: Classification of cerebral lymphomas and glioblastomas featuring luminance distribution analysis.
abstract: Differentiating lymphomas and glioblastomas is important for proper treatment planning. A number of works have been proposed but there are still some problems. For example, many works depend on thresholding a single feature value, which is susceptible to noise. In other cases, experienced observers are required to extract the feature values or to provide some interactions with the system. Even if experts are involved, interobserver variance becomes another problem. In addition, most of the works use only one or a few slice(s) because 3D tumor segmentation is time consuming. In this paper, we propose a tumor classification system that analyzes the luminance distribution of the whole tumor region. Typical cases are classified by the luminance range thresholding and the apparent diffusion coefficients (ADC) thresholding. Nontypical cases are classified by a support vector machine (SVM). Most of the processing elements are semiautomatic. Therefore, even novice users can use the system easily and get the same results as experts. The experiments were conducted using 40 MRI datasets. The classification accuracy of the proposed method was 91.1% without the ADC thresholding and 95.4% with the ADC thresholding. On the other hand, the baseline method, the conventional ADC thresholding, yielded only 67.5% accuracy.
authors.affiliation: School of Electrical and Computer Engineering, Cornell University, Phillips Hall, Ithaca, NY 14853-5401, USA. yamasaki@hal.t.u-tokyo.ac.jp
authors.forename: Toshihiko
authors.initials: T
authors.lastname: Yamasaki
authors.forename: Tsuhan
authors.initials: T
authors.lastname: Chen
authors.forename: Toshinori
authors.initials: T
authors.lastname: Hirai
authors.forename: Ryuji
authors.initials: R
authors.lastname: Murakami
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001932:Brain Neoplasms / Q000145:classification* / Q000175:diagnosis*; D002648:Child; D019295:Computational Biology; D016208:Databases, Factual / Q000706:statistics & numerical data; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D005909:Glioblastoma / Q000145:classification* / Q000175:diagnosis*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data; D008163:Luminescent Measurements / Q000379:methods / Q000706:statistics & numerical data; D008223:Lymphoma / Q000145:classification* / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D060388:Support Vector Machine; D055815:Young Adult