DefinePK

Abstract

In computer aided diagnosis, pre-processing, segmentation, feature extraction and classification are the steps involved. For segmentation the boundary must be defined to get regional information inside the boundary. Ground Glass Opacity (GGO) has ill-defined boundary. Hence, there is a necessity to optimize the boundary of GGO. Once the boundary is optimized, feature extraction and classification of malignant and benign of the particular GGO becomes easy. Using Distance Regularized Level Set Evolution (DRLSE) and Active contour without edges independently, the contour is grown and compared with the model image. The model image was created using the expertise of a Radiologist, applying Mattes Mutual information method. The contour which gives maximum mutual information is concluded as the optimized boundary. Wavelet transform has already been proven its application in identifying pits and cracks of corrosion metals. The same analogy is applied in GGO so that the partly solid and liquid GGOs can be precisely classified as malignant or benign. After optimizing the ill-defined boundary of GGO features of wavelet transformation were extracted along with textural features of mean and variance. Skewness and kurtosis were neglected since they were negligibly small. It is shown that on comparing the growing contour with model image using mattes mutual information, the DRLSE method shows greater results, without leaking compared to Active contour without edges. After extraction of features from wavelet transformation and textural features classification as malignant and benign was done using learning vector quantization (LVQ). On finding the optimized boundary, it is easier to classify the ground glass opacity as diffuse finding or local finding. Hence taking two images where one is taken as malignant and other as benign classification was done as benign and benign by the classifier. The malignant have been identified as benign due to minimum number of images used in training.