This paper presents a novel classification model for biomedical images that involves automatic classification of microscopy and endoscopy images by using joint histogram of intensity and distance. Our proposed joint histogram can adequately capture intensity, texture and shape information (distribution) about the object of interest. We derive a kernel based on the Bhattacharya coefficient of jo...

A clinico-pathological study was performed retrospectively on 62 patients who underwent surgery for renal cell carcinoma between January 1992 and October 1998 at Himeji National Hospital to clarify the prognostic determinants for survival. The median follow-up period was 32 months and the cause-specific survival rates at 1, 3 and 5 years were 86.7, 81.3, 81.3%, respectively. Of the 62 patients,...

Classification of pediatric brain tumors with unusual histologic and clinical features may be a diagnostic challenge to the pathologist. We present a case of a 12-year-old girl with a primary intracranial tumor. The tumor classification was not certain initially, and the site of origin and clinical behavior were unusual. Genomic characterization of the tumor using a Clinical Laboratory Improvem...

The treatment and prognosis for a tumor depend to a large extent on its stage. The goal of this article is to analyze to what extent tumor grading can be performed automatically using the OWLDL description logic language. We focused on the grading of lung tumors. Section 2 is a review of the authoritative cancer ontology, in which we conclude that the NCIT has to be extended in order to perform...

We report a pilot study designed to test elastic light-scattering (ELS) spectroscopy for characterizing normal, tumor, and tumor-infiltrated brain tissues. ELS spectra were measured from 393 sites on 36 ex vivo tissue specimen obtained from 29 patients. We employed and compared the performances of three methods of spectral classification for tissue characterization, including spectral slope ana...

Brain tumor diagnosis is a very crucial task. This system provides an efficient and fast way for diagnosis of the brain tumor. Proposed system consists of multiple phases. First phase consists of texture feature extraction from brain MR images. Second phase classify brain images on the bases of these texture feature using ensemble base classifier. After classification tumor region is extracted ...

In this paper, an automatic support system for brain tumor stage classification using learning machine and for detecting brain tumor during early stages using fuzzy clustering methods is proposed. The fuzzy clustering method is a segmentation technique presented to segment the Magnetic Resonance images for detecting the Brain Tumor during early stages and for examining anatomical structures. Fa...

Gene expression profiles may offer more or additional information than classic morphologic- and histologic-based tumor classification systems. Because the number of tissue samples examined is usually much smaller than the number of genes examined, efficient data reduction and analysis methods are critical. In this report, we propose a principal component and discriminant analysis method of tumo...

Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular...

Cancer is caused by aberrant genetic and epigenetic changes in genes expression. DNA methylation, histone modification, and microRNAs gene deregulation are the most known epigenetic changes in different stages of cancer. Since every tumor has its own specific epigenome, any abnormal pattern is a potential biomarker for classification of different types of tumors. Despite, tumorigenesis, abnorma...