In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-au...

In traditional cancer diagnosis, pathologists examine biopsies to make diagnostic assessments largely based on cell morphology and tissue distribution. However, this is subjective and often leads to considerable variability. On the other hand, computational diagnostic tools enable objective judgments by making use of quantitative measures. This paper presents a systematic survey of the computat...

This paper presents a novel machine learning-based system for the automated diagnosis of breast cancer using mammography images. The proposed employs nature-inspired feature extraction algorithm to accurately identify and highlight salient features within mammographic scans. derived are highly representative effective in distinguishing between malignant benign cases, thus addressing inherent co...

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At same time, breast cancer becomes deadliest among women and can be detected by use of different imaging techniques. Digital mammograms used for earlier identification minimize death rate. But proper has mainly relied on mammography findings results increased false positives. For r...

background: to determine a cut-off point of tpsa and psad to prevent unnecessary invasive cancer-diagnosing tests in the community. methods: this study was performed on 688 consecutive patients referred to our center due to prostatism, suspicious lesions on digital rectal examination and/or elevated serum psa levels. all patients underwent transrectal ultrasound guided biopsies and obtained psa...

Raman spectroscopy is a promising new tool for noninvasive, real-time diagnosis of tissue abnormalities. Here, we show evidence of its application for cancer diagnosis in four distinct tissue types: skin, breast, gastrointestinal tract, and cervix. Multivariate statistical analysis and discrimination algorithms allow for automated classification of the spectra into clinically relevant pathologi...

An approach for cytologic diagnosis of thyroid cancer with the help of automatic morphometry is proposed. This approach is based on a developed computer analyser of images that is aimed at: automated processing and binarization of colour images; automatic raster-to-vector transformation and formation of biological objects; morphometric assessment of biological objects by quantitative parameters...

The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is greatly improved by an early and accurate diagnosis. Several studies have created automated methods to forecast PDAC development utilising various medical imaging modalities. These papers give a general overview the classification, segmentation, or grading many cancer types conventional machine learning techniques hand-en...