Histopathological evaluation plays a crucial role in the process of understanding lung cancer biology. Such evaluation consists in analyzing patterns related with tissue structure and cell morphology to identify the presence of cancer and the associated subtype. This investigation presents a multi-level texture approach to differentiate the two main lung cancer subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SCC), by estimating global spatial patterns in terms of cell density. Such patterns correspond to texture features computed from cell density distribution in a co-occurrence frame. Results using the proposed approach achieved an accuracy of 0.72 and F-score of 0.72.
Speckle noise filtering has been investigated since at least fifty years, this multiplicative and granular interference may be found in any image, i.e, Synthetic Aperture Radar (SAR), optical coherence tomography, and of course, medical ultrasound imaging. Speckle noise is produced by structural characteristics of materials, in case of the ultrasound imaging case, by small structural irregularities. This work proposes a novel speckle noise filtering strategy using a bank of morphological multi-scale filters that captures anisotropic information and additionally preserves cardiac structures. This method is compared against commonly used filters, namely: Anisotropic Diffusion Filter (ADMSS), Non-Local Means Filter (NLMF) and Detail Preserving Anisotropic Diffusion Filter (DPAD).
The non-small cell lung cancer (NSCLC) is the most frequent with about 80% of new cases and it is subdivided into adenocarcinoma, squamous cell and large cell carcinomas. Several studies have demonstrated the relevance of identifying NSCLC cancer subtype for prognosis and treatment. This work presents a classification approach for NSCLC subtypes in computed tomography images based on a multi-scale texture analysis. For doing so, gradients over the difference between multi-scale homogeneity textures was computed to build feature descriptors. Binary classifications were performed for the three NSCLC cancer subtypes under a 10-fold cross-validation scheme, and the best results were obtained for adenocarcinoma vs. squamous cell carcinoma, with an area under the curve of 80% and an accuracy of 77; 4%. The results demonstrate that CT is an useful source of information for extracting patterns that allow to identify tissue changes and correlate them with histological outcome.