The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing
Description:
International Journal of Emerging Technologies in Computational and Applied Sciences, 7(1), December 2013-
February, 2014, pp. 08-14
Tessamma, Thomas; Ananda Resmi, S(ACEEE, November , 2010)
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Abstract:
Low grade and High grade Gliomas are tumors
that originate in the glial cells. The main challenge in brain
tumor diagnosis is whether a tumor is benign or malignant,
primary or metastatic and low or high grade. Based on the
patient's MRI, a radiologist could not differentiate whether it
is a low grade Glioma or a high grade Glioma. Because both
of these are almost visually similar, autopsy confirms the
diagnosis of low grade with high-grade and infiltrative
features. In this paper, textural description of Grade I and
grade III Glioma are extracted using First order statistics and
Gray Level Co-occurance Matrix Method (GLCM). Textural
features are extracted from 16X16 sub image of the
segmented Region of Interest(ROI) .In the proposed method,
first order statistical features such as contrast, Intensity ,
Entropy, Kurtosis and spectral energy and GLCM features
extracted were showed promising results. The ranges of these
first order statistics and GLCM based features extracted are
highly discriminant between grade I and Grade III. In this
study which gives statistical textural information of grade I
and grade III Glioma which is very useful for further
classification and analysis and thus assisting Radiologist in
greater extent.
Description:
Int. J. of Recent Trends in Engineering and Technology, Vol. 4, No. 3, Nov 2010