Abstract:
This thesis presents a novel Sparse Representation-based Classifier (SRC) that
provides superior performance in terms of high Area Under the roc Curve (AUC)
in classifying benign and malignant lesions of breasts captured in ultrasound images.
Although such a classifier was initially proposed for face recognition, the use of this
has been proposed in medical diagnosis from ultrasonic images in this dissertation
for the first time. The classifier is based on `1-norm based sparse representation of
a patient’s test data in terms of linear combination of the features of the benign and
malignant test lesions available in the training set. The proposed classifier uses an
index called Sparsity Rank (SR) for the classification obtained from the normalized
energy of the weights as a linear combination of the global sparse representation
of the ultrasound images of the training set. The performance of the classifier is
further enhanced to a great extent by two ways; first, by the use of a method that
intelligently combines the features extracted from the multiple ultrasound scan of
the same patient, and the second, by using the reduced feature set. The combining
principle of the multiple data scans is based on averaging of the SRs of all the scans.
The near-to-optimal feature set is obtained by a suboptimal strategy to evade
the time exhaustive brute force approach that has a combinatorial search space.With all the enhancements an AUC of 0:9754 has been achieved, when training and
testing sets are chosen by leave-one-out approach from the data set.
