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Abstract:
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Cerebral glioma is the most prevalent primary brain tumor, which are
classified broadly into low and high grades according to the degree of malignancy.
High grade gliomas are highly malignant which possess a poor prognosis, and the
patients survive less than eighteen months after diagnosis. Low grade gliomas are
slow growing, least malignant and has better response to therapy. To date,
histological grading is used as the standard technique for diagnosis, treatment
planning and survival prediction.
The main objective of this thesis is to propose novel methods for automatic
extraction of low and high grade glioma and other brain tissues, grade detection
techniques for glioma using conventional magnetic resonance imaging (MRI)
modalities and 3D modelling of glioma from segmented tumor slices in order to
assess the growth rate of tumors. Two new methods are developed for extracting
tumor regions, of which the second method, named as Adaptive Gray level
Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and
grey matter from T1 FLAIR an T2 weighted images. The methods were validated
with manual Ground truth images, which showed promising results. The developed
methods were compared with widely used Fuzzy c-means clustering technique and
the robustness of the algorithm with respect to noise is also checked for different
noise levels. Image texture can provide significant information on the
(ab)normality of tissue, and this thesis expands this idea to tumour texture grading
and detection. Based on the thresholds of discriminant first order and gray level cooccurrence
matrix based second order statistical features three feature sets were
formulated and a decision system was developed for grade detection of glioma
from conventional T2 weighted MRI modality.The quantitative performance
analysis using ROC curve showed 99.03% accuracy for distinguishing between
advanced (aggressive) and early stage (non-aggressive) malignant glioma. The
developed brain texture analysis techniques can improve the physician’s ability to
detect and analyse pathologies leading to a more reliable diagnosis and treatment of
disease. The segmented tumors were also used for volumetric modelling of tumors
which can provide an idea of the growth rate of tumor; this can be used for
assessing response to therapy and patient prognosis. |