Colour model analysis for microscopic image processing

This article presents a comparative study between different colour models (RGB, HSI and CIEL*a*b*) applied to a very large microscopic image analysis. Such analysis of different colour models is needed in order to carry out a successful detection and therefore a classification of different regions of interest (ROIs) within the image. This, in turn, allows both distinguishing possible ROIs and retrieving their proper colour for further ROI analysis. This analysis is not commonly done in many biomedical applications that deal with colour images. Other important aspects is the computational cost of the different processing algorithms according to the colour model. This work takes these aspects into consideration to choose the best colour model tailored to the microscopic stain and tissue type under consideration and to obtain a successful processing of the histological image

TMA Vessel Segmentation Based on Color and Morphological Features: Application to Angiogenesis Research

Given that angiogenesis and lymphangiogenesis are strongly related to prognosis in neoplastic and other pathologies and that many methods exist that provide different results, we aim to construct a morphometric tool allowing us to measure different aspects of the shape and size of vascular vessels in a complete and accurate way. The developed tool presented is based on vessel closing which is an essential property to properly characterize the size and the shape of vascular and lymphatic vessels. The method is fast and accurate improving existing tools for angiogenesis analysis. The tool also improves the accuracy of vascular density measurements, since the set of endothelial cells forming a vessel is considered as a single object

Multiple vertebral fractures after suspension of denosumab. A series of 56 cases

Background: Denosumab is a monoclonal antibody approved for the treatment of postmenopausal osteoporosis. The withdrawal of denosumab produces an abrupt loss of bone mineral density and may cause multiple vertebral fractures (MVF). Objective: The objective of this study is to study the clinical, biochemical, and densitometric characteristics in a large series of postmenopausal women who suffered MVF after denosumab withdrawal. Likewise, we try to identify those factors related to the presence of a greater number of vertebral fractures (VF). Patients and methods: Fifty-six patients (54 women) who suffered MVF after receiving denosumab at least for three consecutive years and abruptly suspended it. A clinical examination was carried out. Biochemical bone remodelling markers (BBRM) and bone densitometry at the lumbar spine and proximal femur were measured. VF were diagnosed by magnetic resonance imaging MRI, X-ray, or both at dorsal and lumbar spine. Results: Fifty-six patients presented a total of 192 VF. 41 patients (73.2%) had not previously suffered VF. After discontinuation of the drug, a statistically significant increase in the BBRM was observed. In the multivariate analysis, only the time that denosumab was previously received was associated with the presence of a greater number of VF (P = .04). Conclusions: We present the series with the largest number of patients collected to date. 56 patients accumulated 192 new VF. After the suspension of denosumab and the production of MVF, there was an increase in the serum values of the BBRM. The time of denosumab use was the only parameter associated with a greater number of fractures

A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering

With modern automated microscopes and digital cameras, pathologists no longer have to examine samples looking through microscope binoculars. Instead, the slide is digitized to an image, which can then be examined on a screen. This creates the possibility for computers to analyze the image. In this work, a fully automated approach to region of interest (ROI) segmentation in prostate biopsy images is proposed. This will allow the pathologists to focus on the most important areas of the image. The method proposed is based on level-set and mean filtering techniques for lumen centered expansion and cell density localization respectively. The novelty of the technique lies in the ability to detect complete ROIs, where a ROI is composed by the conjunction of three different structures, that is, lumen, cytoplasm, and cells, as well as regions with a high density of cells. The method is capable of dealing with full biopsies digitized at different magnifications. In this paper, results are shown with a set of 100 H and E slides, digitized at 5×, and ranging from 12 MB to 500 MB. The tests carried out show an average specificity above 99% across the board and average sensitivities of 95% and 80%, respectively, for the lumen centered expansion and cell density localization. The algorithms were also tested with images at 10× magnification (up to 1228 MB) obtaining similar results

Breast density classification to reduce false positives in CADe systems

[Abstract] This paper describes a novel weighted voting tree classification scheme for breast density classification. Breast parenchymal density is an important risk factor in breast cancer. Moreover, it is known that mammogram interpretation is more difficult when dense tissue is involved. Therefore, automated breast density classification may aid in breast lesion detection and analysis. Several classification methods have been compared and a novel hierarchical classification procedure of combined classifiers with linear discriminant analysis (LDA) is proposed as the best solution to classify the mammograms into the four BIRADS tissue classes. The classification scheme is based on 298 texture features. Statistical analysis to test the normality and homoscedasticity of the data was carried out for feature selection. Thus, only features that are influenced by the tissue type were considered. The novel classification techniques have been incorporated into a CADe system to drive the detection algorithms and tested with 1459 images. The results obtained on the 322 screen-film mammograms (SFM) of the mini-MIAS dataset show that 99.75% of samples were correctly classified. On the 1137 full-field digital mammograms (FFDM) dataset results show 91.58% agreement. The results of the lesion detection algorithms were obtained from modules integrated within the CADe system developed by the authors and show that using breast tissue classification prior to lesion detection leads to an improvement of the detection results. The tools enhance the detectability of lesions and they are able to distinguish their local attenuation without local tissue density constraints

Abstract Expressive Robotic Face for Interaction

This paper describes the current development status of a robot head with basic interactional abilities. On a theoretical level we propose an explanation for the lack of robustness implicit in the so-called social robots. The fact is that our social abilities are mainly unconscious to us. This lack of knowledge about the form of the solution to these abilities leads to a fragile behaviour. Therefore, the engineering point of view must be seriously taken into account, and not only insights taken from human disciplines like developmental psychology or ethology. Our robot, built upon this idea, does not have a definite task, except to interact with people. Its perceptual abilities include sound localization, omnidirectional vision, face detection, an attention module, memory and habituation. The robot has facial features that can display basic emotional expressions, and it can speak canned text through a TTS. The robot’s behavior is controlled by an action selection module, reflexes and a basic emotional module.