Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann

Melhorias na segmentação de pele humana em imagens digitais

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Segmentação de pele humana possui diversas aplicações nas áreas de visão computacional e reconhecimento de padrões, cujo propósito principal é distinguir regiões de pele e não pele em imagens. Apesar do elevado número de métodos disponíveis na literatura, a segmentação de pele com precisão ainda é uma tarefa desafiadora. Muitos métodos contam somente com a informação de cor, o que não discrimina completamente as regiões da imagem devido a variações nas condições de iluminação e à ambiguidade entre a cor da pele e do plano de fundo. Dessa forma, há ainda a demanda em melhorar a segmentação. Este trabalho apresenta três contribuições com respeito a essa necessidade. A primeira é um método autocontido para segmentação adaptativa de pele que faz uso de análise espacial para produzir regiões nas quais a cor da pele é estimada e, dessa forma, ajusta o padrão da cor para uma imagem em particular. A segunda é a introdução da detecção de saliência para, combinada com detectores de pele baseados em cor, realizar a remoção do plano de fundo, o que elimina muitas regiões de não pele. A terceira é uma melhoria baseada em textura utilizando superpixels para capturar energia de regiões na imagem filtrada, que é então utilizada para caracterizar regiões de não pele e assim eliminar a ambiguidade da cor adicionando um segundo voto. Resultados experimentais obtidos em bases de dados públicas comprovam uma melhoria significativa nos métodos propostos para segmentação de pele humana em comparação com abordagens disponíveis na literaturaAbstract: Human skin segmentation has several applications on computer vision and pattern recognition fields, whose main purpose is to distinguish skin and non-skin regions. Despite the large number of methods available in the literature, accurate skin segmentation is still a challenging task. Many methods rely only on color information, which does not completely discriminate the image regions due to variations in lighting conditions and ambiguity between skin and background color. Therefore, there is still demand to improve the segmentation process. Three main contributions toward this need are presented in this work. The first is a self-contained method for adaptive skin segmentation that makes use of spatial analysis to produce regions from which the overall skin color can be estimated and such that the color model is adjusted to a particular image. The second is the combination of saliency detection with color skin segmentation, which performs a background removal to eliminate non-skin regions. The third is a texture-based improvement using superpixels to capture energy of regions in the filtered image, employed to characterize non-skin regions and thus eliminate color ambiguity adding a second vote. Experimental results on public data sets demonstrate a significant improvement of the proposed methods for human skin segmentation over state-of-the-art approachesMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Fully automated breast segmentation on spiral breast computed tomography images

INTRODUCTION The quantification of the amount of the glandular tissue and breast density is important to assess breast cancer risk. Novel photon-counting breast computed tomography (CT) technology has the potential to quantify them. For accurate analysis, a dedicated method to segment the breast components-the adipose and glandular tissue, skin, pectoralis muscle, skinfold section, rib, and implant-is required. We propose a fully automated breast segmentation method for breast CT images. METHODS The framework consists of four parts: (1) investigate, (2) segment the components excluding adipose and glandular tissue, (3) assess the breast density, and (4) iteratively segment the glandular tissue according to the estimated density. For the method, adapted seeded watershed and region growing algorithm were dedicatedly developed for the breast CT images and optimized on 68 breast images. The segmentation performance was qualitatively (five-point Likert scale) and quantitatively (Dice similarity coefficient [DSC] and difference coefficient [DC]) demonstrated according to human reading by experienced radiologists. RESULTS The performance evaluation on each component and overall segmentation for 17 breast CT images resulted in DSCs ranging 0.90-0.97 and in DCs 0.01-0.08. The readers rated 4.5-4.8 (5 highest score) with an excellent inter-reader agreement. The breast density varied by 3.7%-7.1% when including mis-segmented muscle or skin. CONCLUSION The automatic segmentation results coincided with the human expert's reading. The accurate segmentation is important to avoid the significant bias in breast density analysis. Our method enables accurate quantification of the breast density and amount of the glandular tissue that is directly related to breast cancer risk

Fast parameter-free region growing segmentation with application to surgical planning

In this paper, we propose a self-assessed adaptive region growing segmentation algorithm. In the context of an experimental virtual-reality surgical planning software platform, our method successfully delineates main tissues relevant for reconstructive surgery, such as fat, muscle, and bone. We rely on a self-tuning approach to deal with a great variety of imaging conditions requiring limited user intervention (one seed). The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing region, and the stopping criterion is adapted to the noise level in the dataset thanks to the sampling strategy used for the assessment function. Sampling is referred to the statistics of a neighborhood around the seed(s), so that the sampling period becomes greater when images are noisier, resulting in the acquisition of a lower frequency version of the contrast function. Validation is provided for synthetic images, as well as real CT datasets. For the CT test images, validation is referred to manual delineations for 10 cases and to subjective assessment for another 35. High values of sensitivity and specificity, as well as Dice’s coefficient and Jaccard’s index on one hand, and satisfactory subjective evaluation on the other hand, prove the robustness of our contrast-based measure, even suggesting suitability for calibration of other region-based segmentation algorithms

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. (C) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

Data efficient deep learning for medical image analysis: A survey

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.Comment: Under Revie

Computer-Aided Assessment of Tuberculosis with Radiological Imaging: From rule-based methods to Deep Learning

Mención Internacional en el título de doctorTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb.) that produces pulmonary damage due to its airborne nature. This fact facilitates the disease fast-spreading, which, according to the World Health Organization (WHO), in 2021 caused 1.2 million deaths and 9.9 million new cases. Traditionally, TB has been considered a binary disease (latent/active) due to the limited specificity of the traditional diagnostic tests. Such a simple model causes difficulties in the longitudinal assessment of pulmonary affectation needed for the development of novel drugs and to control the spread of the disease. Fortunately, X-Ray Computed Tomography (CT) images enable capturing specific manifestations of TB that are undetectable using regular diagnostic tests, which suffer from limited specificity. In conventional workflows, expert radiologists inspect the CT images. However, this procedure is unfeasible to process the thousands of volume images belonging to the different TB animal models and humans required for a suitable (pre-)clinical trial. To achieve suitable results, automatization of different image analysis processes is a must to quantify TB. It is also advisable to measure the uncertainty associated with this process and model causal relationships between the specific mechanisms that characterize each animal model and its level of damage. Thus, in this thesis, we introduce a set of novel methods based on the state of the art Artificial Intelligence (AI) and Computer Vision (CV). Initially, we present an algorithm to assess Pathological Lung Segmentation (PLS) employing an unsupervised rule-based model which was traditionally considered a needed step before biomarker extraction. This procedure allows robust segmentation in a Mtb. infection model (Dice Similarity Coefficient, DSC, 94%±4%, Hausdorff Distance, HD, 8.64mm±7.36mm) of damaged lungs with lesions attached to the parenchyma and affected by respiratory movement artefacts. Next, a Gaussian Mixture Model ruled by an Expectation-Maximization (EM) algorithm is employed to automatically quantify the burden of Mtb.using biomarkers extracted from the segmented CT images. This approach achieves a strong correlation (R2 ≈ 0.8) between our automatic method and manual extraction. Consequently, Chapter 3 introduces a model to automate the identification of TB lesions and the characterization of disease progression. To this aim, the method employs the Statistical Region Merging algorithm to detect lesions subsequently characterized by texture features that feed a Random Forest (RF) estimator. The proposed procedure enables a selection of a simple but powerful model able to classify abnormal tissue. The latest works base their methodology on Deep Learning (DL). Chapter 4 extends the classification of TB lesions. Namely, we introduce a computational model to infer TB manifestations present in each lung lobe of CT scans by employing the associated radiologist reports as ground truth. We do so instead of using the classical manually delimited segmentation masks. The model adjusts the three-dimensional architecture, V-Net, to a multitask classification context in which loss function is weighted by homoscedastic uncertainty. Besides, the method employs Self-Normalizing Neural Networks (SNNs) for regularization. Our results are promising with a Root Mean Square Error of 1.14 in the number of nodules and F1-scores above 0.85 for the most prevalent TB lesions (i.e., conglomerations, cavitations, consolidations, trees in bud) when considering the whole lung. In Chapter 5, we present a DL model capable of extracting disentangled information from images of different animal models, as well as information of the mechanisms that generate the CT volumes. The method provides the segmentation mask of axial slices from three animal models of different species employing a single trained architecture. It also infers the level of TB damage and generates counterfactual images. So, with this methodology, we offer an alternative to promote generalization and explainable AI models. To sum up, the thesis presents a collection of valuable tools to automate the quantification of pathological lungs and moreover extend the methodology to provide more explainable results which are vital for drug development purposes. Chapter 6 elaborates on these conclusions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidenta: María Jesús Ledesma Carbayo.- Secretario: David Expósito Singh.- Vocal: Clarisa Sánchez Gutiérre