Abstract
Abstract Esophageal squamous cell carcinoma (ESCC) is the most prevalent malignancy of the esophagus with a very poor prognosis. Nevertheless, squamous cell dysplasia (ESD) has been identified as the only histological precursors of ESCC. Since, tissue alterations are slight in the early stage of ESD, human diagnosis is subjective. Hence, this work presents a first computer-aided system to differentiate low-grade dysplasia (LGD) from normal esophageal mucosa according to Vienna grading system, which is the most widespread method for histological grading of esophagus tissues. We captured microscopic images of a well-oriented region of Normal and LGD biopsies to characterize the architectural and cytological properties of specimens based on the computational analysis. We produced two sets of enhanced images. Then, by considering the fractal concept, we defined a new scale-dependent function in the generalized fractal dimension formulation to include the special information of both preprocessed images together. Then, for each image, a pattern was computed from variations of tissue fractal geometry across the pathway of dysplasia development. We proposed features extracted from these patterns to classify deviations of tissue characteristics from the normal stage. This method successfully differentiated the two diagnosis classes with statistical significance and high performance (accuracy = 97.78% ± 0.05, p
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