Abstract
The ability to distinguish adenomas from benign polyps during routine colonoscopy can be challenging. High-resolution microendoscopy (HRME) is an advanced optical imaging technique allowing subcellular imaging of the colorectal mucosa. This study aimed to develop quantitative HRME image analysis algorithms based on pathological and architectural features. In addition, it aimed to evaluate the automated classification algorithms performance to accurately categorize neoplastic (tubular adenoma, tubulovillous adenoma and cancer) and non-neoplastic (normal, hyperplastic) mucosa in the colon.
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