A dynamic parallel approach to recognize tubular breast cancer for TMA image building

Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously, allowing faster analysis and considerably reducing costs incurred in staining. The presented work supports the pre-array phase of this technique, i.e. the automatic discrimination between normal and pathological regions within the analyzed tissues, and it works in the specific context of tubular breast cancer. The diagnosis is performed by automatically analyzing specific morphological features of the breast samples, in order to define if tissues present a normal behavior either they show pathological characteristics, in particular the absence of a double layer of cells around the lumen or the decay of a regular glands-and-lobules structure. Tissue structure is investigated through a parallel image processing algorithm, which performs the extraction of morphological parameters from the acquired images and compares them to experimentally validated threshold values. The input image is divided into a number of independent sub-images, to be singularly analyzed and labeled according to the result of the computed diagnosis. The time spent to actually analyze each sub-image generally varies depending on the pathological characteristics of each part of the tissue. In order to properly manage and exploit this feature of the algorithm, the analysis of the sub-images is dynamically dispatched among parallel processes. Experimental results, carried on by exploiting the parallel paradigm, certify the actual improvement in the execution time, leading to almost linear speed-up values on the actual tissue elaboration.