A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining.

Immunohistochemistry is a powerful technique that is widely used in biomedical research and clinics; it allows one to determine the expression levels of some proteins of interest in tissue samples using color intensity due to the expression of biomarkers with specific antibodies. As such, immunohistochemical images are complex and their features are difficult to quantify. Recently, we proposed a novel method, including a first separation stage based on non-negative matrix factorization (NMF), that achieved good results.

A Method for Unsupervised Semi-Quantification of Inmunohistochemical Staining with Beta Divergences.

In many research laboratories, it is essential to determine the relative expression levels of some proteins of interest in tissue samples. The semi-quantitative scoring of a set of images consists of establishing a scale of scores ranging from zero or one to a maximum number set by the researcher and assigning a score to each image that should represent some predefined characteristic of the IHC staining, such as its intensity. However, manual scoring depends on the judgment of an observer and therefore exposes the assessment to a certain level of bias.

Centroid-Based Clustering with -Divergences.

Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use. In recent years, most studies focused on including several divergence measures in the traditional hard -means algorithm.

Automatic classification of tissue malignancy for breast carcinoma diagnosis.

Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity.

Automatic CDR Estimation for Early Glaucoma Diagnosis.

Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images.

Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.

Diabetic retinopathy (DR) is leading cause of blindness among diabetic patients. Recognition of severity level is required by ophthalmologists to early detect and diagnose the DR. However, it is a challenging task for both medical experts and computer-aided diagnosis systems due to requiring extensive domain expert knowledge.

Burn depth analysis using multidimensional scaling applied to psychophysical experiment data.

In this paper a psychophysical experiment and a multidimensional scaling (MDS) analysis are undergone to determine the physical characteristics that physicians employ to diagnose a burn depth. Subsequently, these characteristics are translated into mathematical features, correlated with these physical characteristics analysis. Finally, a study to verify the ability of these mathematical features to classify burns is performed.

[Automated detection of microaneurysms by using region growing and Fuzzy Artmap neural network].

Objective: To assess whether the methodological changes of this new algorithm improves the results of a previously presented strategy.

Methods: We enhance the image and filter out the green channel of the digital color retinography. Multitolerance thresholding was applied to obtain candidate points and make a seed growing region by varying intensities.

Computer-aided pattern classification system for dermoscopy images.

Background: Computer-aided pattern classification of melanoma and other pigmented skin lesions is one of the most important tasks for clinical diagnosis. To differentiate between benign and malignant lesions, the extraction of color, architectural order, symmetry of pattern and homogeneity (CASH) is a challenging task.

Methods: In this article, a novel pattern classification system (PCS) based on the clinical CASH rule is presented to classify among six classes of patterns.

[Automatic detection of microaneurysms in colour fundus images].

Purpose: We present the development of a tool for the automatic detection of microaneurysms and its clinical evaluation. The intention of this tool is to facilitate the diagnosis of diabetic retinopathy in general screening programs.

Method: The designed and developed tool consists of three stages of processing: 1) Obtaining of the basic image of eye with the retinal camera, inverted image on the green channel, and a high-pass filter of the image.

A system for 3D representation of burns and calculation of burnt skin area.

In this paper a computer-based system for burnt surface area estimation (BAI), is presented. First, a 3D model of a patient, adapted to age, weight, gender and constitution is created. On this 3D model, physicians represent both burns as well as burn depth allowing the burnt surface area to be automatically calculated by the system.

Unsupervised skin lesions border detection via two-dimensional image analysis.

The skin cancer was analyzed by dermoscopy helpful for dermatologists. The classification of melanoma and carcinoma such as basal cell, squamous cell, and merkel cell carcinomas tumors can be increased the sensitivity and specificity. The detection of an automated border is an important step for the correctness of subsequent phases in the computerized melanoma recognition systems.

[Automatic detection of vessels in color fundus images].

Purpose: The main purpose of the paper is to evaluate an automated method for blood vessels segmentation in color fundus images, due to its important role in the diagnosis of several pathologies such as diabetes. The final objective is to introduce the algorithm into a Computer Aided Diagnosis (CAD) tool that would be available in those local medical centers without specialists.

Method: An automated method for blood vessels segmentation in color fundus images was implemented and tested.