COMPARATIVE STUDY FOR MELANOMA SEGMENTATION IN SKIN LESION IMAGES

Melanoma is the leading cause of fatalities among skin can-cers and the discovery of the pathology in the early stagesis essential to increase the chances of cure. Computationalmethods through medical imaging are being developed tofacilitate the detection of melanoma. To interpret informa-tion in these images eciently, it is necessary to isolate theaected region. In our research, a comparison was made be-tween segmentation techniques, rstly a method based onthe Otsu algorithm, secondly the K-means clustering algo-rithm and nally,the U-net deep learning was developed.The tests performed on the PH2 images base had promisingresults, especially U-net

Radial feature descriptors for cell classification and recommendation.

This paper introduces computational tools for cell classification into normal and abnormal, as well as content-based-image-retrieval (CBIR) for cell recommendation. It also proposes the radial feature descriptors (RFD), which define evenly interspaced segments around the nucleus, and proportional to the convexity of the nuclear boundary. Experiments consider Herlev and CRIC image databases as input to classification via Random Forest and bootstrap; we compare 14 different feature sets by means of False Negative Rate (FNR) and Kappa (k), obtaining FNR = 0.02 and k = 0.89 for Herlev, and FNR = 0.14 and k = 0.78 for CRIC. Next, we sort and rank cell images using convolutional neural networks and evaluate performance with the Mean Average Precision (MAP), achieving MAP = 0.84 and MAP = 0.82 for Herlev and CRIC, respectively. Cell classification show encouraging results regarding RFD, including its sensitivity to intensity variation around the nuclear membrane as it bypasses cytoplasm segmentation

Reverse image search for scientific data within and beyond the visible spectrum.

The explosion in the rate, quality and diversity of image acquisition instruments has propelled the de- velopment of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper intro- duces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR , a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released

Deep learning for cell image segmentation and ranking.

Ninety years after its invention, the Pap test continues to be the most used method for the early identification of cervical precancerous lesions. In this test, the cytopathologists look for microscopic abnormalities in and around the cells, which is a time-consuming and prone to human error task. This paper introduces computational tools for cytological analysis that incorporate cell segmentation deep learning techniques. These techniques are capable of processing both free-lying and clumps of abnormal cells with a high overlapping rate from digitized images of conventional Pap smears. Our methodology employs a preprocessing step that discards images with a low probability of containing abnormal cells without prior segmentation and, therefore, performs faster when compared with the existing methods. Also, it ranks outputs based on the likelihood of the images to contain abnormal cells. We evaluate our methodology on an image database of conventional Pap smears from real scenarios, with 108 fields-of-view containing at least one abnormal cell and 86 containing only normal cells, corresponding to millions of cells. Our results show that the proposed approach achieves accurate results (MAP?=?0.936), runs faster than existing methods, and it is robust to the presence of white blood cells, and other contaminants