Enhanced histogram equalization based nodule enhancement and neural network based detection for chest x-ray radiographs

Lung cancer is the malignant tumors, which maximizes the speed of incidence and death rate and it will in the shape of spherical nodules in a conventional radiograph. Moreover, few lung nodules can’t be identified, since it overlaps with the normal anatomic structures like ribs and clavicles. Earlier, Rib suppression is executed to rectify this issue, which works according to the Linear Discriminant Analysis (LDA) and it is brought-into maximize the visibility of lung nodules. With this motivation, initially, it is constructed through LDA; then, the pixel intensity of lung nodule is measure through the usage of the Enhanced Histogram Equalization and it is included to the subtracted image of original image and rib model to improve the original brightness; at last, the border of ribs is recognized and smoothed, is done through mean function is measure through the use of pixel intensity. The white nodule-likeness extraction is the second step. Texture feature extraction is done for Continuous Wavelet Transform and the multi-scale Convergence-index filter. In order to create the white nodule-likeness map (WNLM), Neural Network classifier is utilized. Candidate extraction on WNLM using Laplace of Gaussian blob detection method is the third step. To estimate the rib suppression method, the JSRT database and dual-energy images were employed. The experiment represents when the method is enforced on the test images, the ribs can be suppressed significantly.

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