Lung tissue evaluation detecting and measuring morphological characteristics of cell regions

The goal of this study is to develop an automated, accurate and time efficient image processing algorithmic scheme, capable of segmenting lung tissue slides and quantitatively detecting any possible morphological characteristic that may differentiate healthy cells from adenocarcinoma. Microscopy images are segmented into the key regions via a proposed clever, sequential fusion methodology, combining image clustering, the watershed transform and mathematical morphology and analyzed utilizing an innovative tissue evaluation approach based on quantitative assessments of the extracted cell regions shape and size. The preliminary results of this work indicate that it is possible to discriminate healthy cells from cancerous ones considering their overall morphology within the tissue and measuring possible indices that may reveal an evolving neoplasia, a tumor growth or a malfunction in cell proliferation. Applying the proposed method to a much larger and more variform dataset is our next plan for the upcoming future in order to validate and ensure the robustness and accuracy of the proposed classification scheme, making it an extremely valuable assisting tool for medical experts for cancer diagnosis and prognosis.