Differentiating cerebral lymphomas and GBMs featuring luminance distribution analysis

Abstract

Differentiating lymphomas and glioblastoma multiformes (GBMs) 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. Non-typical cases that do not get along with such simple thresholding can be found easily. In other cases, experienced observers are required to extract the feature values or to provide some interactions to the system, which is costly. Even if experts are involved, inter-observer variance becomes another problem. In addition, most of the works use only one or a few slice(s) because 3D tumor segmentation is difficult and time-consuming. In this paper, we propose a tumor classification system that analyzes the luminance distribution of the whole tumor region. The 3D MRIs are segmented within a few tens of seconds by using our fast 3D segmentation algorithm. Then, the luminance histogram of the whole tumor region is generated. The typical cases are classified by the histogram range thresholding and the apparent diffusion coefficients (ADC) thresholding. The non-typical cases are learned and classified by a support vector machine (SVM). Most of the processing elements are semi-automatic except for the ADC value extraction. Therefore, even novice users can use the system easily and get almost the same results as experts. The experiments were conducted using 40 MRI datasets (20 lymphomas and 20 GBMs) with non-typical cases. 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.