Semi-supervised learning approaches for predicting semantic characteristics of lung nodules
Intelligent Decision Technologies - Special issue on advances in medical intelligent decision support systems
Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor
Computer Methods and Programs in Biomedicine
| Hi-index | 0.00 |
In this paper we present a technique for recognizing the lung nodules for different diagnosis of lung cancer based on CT images. Nodule detection is carried in the following steps: pre- processing using wavelet technique, biorthogonal wavelet is used for image enhancement. The enhanced image is subjected to Bi-Histogram equalization. The resultant image is more accurate and sharp. The enhanced image is binarised using the thresholding. Then the binarised image is subjected to Morphological transform. The filtered image is segmented and features are extracted. The extracted features are given to the fuzzy inference systems (FIS). The fuzzy system finds the severity of the lung nodules based on the IF-THEN rules. Key words: wavelet Technique, contrast enhancement, bihistogram equalization, morphology, fuzzy inference system (FIS).