0000000000214722
AUTHOR
Esther Dura
Functional statistics based method for the evaluation of the registration of sequences of 3D perfusion MR images
Accurate registration of medical images is a necessary task for several important diagnosis techniques. Nevertheless, it is a difficult challenge due to movement of the patient, deformations, noise in the signal, etc. Besides, evaluation of the quality of the performed registration is also troublesome, specially when no golden pattern (true result) is available and/or when the signal values may have changed between successive images/volumes to be registered.
Towards a mean body for apparel design
This paper focuses on shape average with applications to the apparel industry. Apparel industry uses a consensus sizing system; its major concern is to fit most of the population into it. Since anthropometric measures do not grow linearly, it is important to find prototypes to accurately represent each size. This is done using random compact mean sets, obtained from a cloud of 3D points given by a scanner and applying to the sample a previous definition of mean set. Additionally, two approaches to define confidence sets are introduced. The methodology is applied to data obtained from a real anthropometric survey. This paper has been partially supported by the following grants: TIN2009-14392…
Mean sets for building 3D probabilistic liver atlas from perfusion MR images
This paper is concerned with liver atlas construction. One of the most important issues in the framework of computational abdominal anatomy is to define an atlas that provides a priori information for common medical task such as registration and segmentation. Unlike other approaches already proposed so far (to our knowledge), in this paper we propose to use the concept of random compact mean set to build probabilistic liver atlases. To accomplish this task a two-tier process was carried out. First a set of 3D images was manually segmented by a physician. We see the different 3D segmented shapes as a realization of a random compact set. Secondly, elements of two known definitions of mean set…
Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Preliminary results
Spine is a structure commonly involved in several prevalent diseases. In clinical diagnosis, therapy, and surgical intervention, the identification and segmentation of the vertebral bodies are crucial steps. However, automatic and detailed segmentation of vertebrae is a challenging task, especially due to the proximity of the vertebrae to the corresponding ribs and other structures such as blood vessels. In this study, to overcome these problems, a probabilistic atlas of the spine, including cervical, thoracic and lumbar vertebrae has been built to introduce anatomical knowledge in the segmentation process, aiming to deal with overlapping gray levels and the proximity to other structures. F…
A local level set method for liver segmentation in functional MR imaging
Functional Magnetic Resonance (fMR) is a medical image technique in which a contrast is injected in the vascular system so that blood diffusion along it can be observed as variations of the signal intensity. The uptake variations of the contrast agent are used in early detection of tumorous tissue. For the diagnostic to be accurate, successive volumes must be correctly registered. For binary registration prior segmentation of the 3D fMR data is required. Here we present a local 3D level-set segmentation method which preserves details and edges, along with its multi-scale version which has the advantage of a great acceleration with respect to the single-scale version. Results of liver segmen…
Image Processing Techniques For the Detection and Classification of Man Made Objects in Side-Scan Sonar Images
This is a review chapter that surveys past work in, and the recent status of image processing and other related techniques involved in the detection and classification of man made objects in side scan sonar images. Side scan sonar is a readily, available and cheap device which has found increasing applications, specially for military purposes such as Computer Aided Detection (CAD) and Classification (CAC) of mines. Therefore the main focus of the chapter is on this topic. The list of references is sufficiently complete to include most past and recent publications in the open refereed literature. Although side scan sonar displays many features similar to an optical sensor from a purely image…
The Future We Want: a Learning Experience to Promote SDGs in Higher Education from the United Nations and University of Valencia
This article shares the strategy for mainstreaming the Sustainable Development Goals (SDGs) at the University of Valencia (UV), which, although limited in its scale, may compel other Higher Education Institutions to think in technological and social progress aligned with the 2030 Agenda. It explicates a process driven by the UV, on the occasion of the 75th anniversary of the United Nations (UN), and in collaboration with the Service for Geospatial, Information, and Telecommunications Technologies from the UN Support Base in Valencia (Spain) to prepare the online event: “The United Nations We Want”. It was the culmination of a collaborative project between students and faculties from differe…
Evaluation of the registration of temporal series of contrast-enhanced perfusion magnetic resonance 3D images of the liver.
The registration of 2D and 3D images is one of the key tasks in medical image processing and analysis. Accurate registration is a crucial preprocessing step for many tasks; consequently, the evaluation of its accuracy becomes necessary. Unfortunately, this is a difficult task, especially when no golden pattern (true result) is available and when the signal values may have changed between successive images to be registered. This is the case this paper deals with: we have a series of 3D images, magnetic resonance images (MRI) of the liver and adjacent areas that have to be registered. They have been taken while a contrast is diffused through the liver tissue, so intensity of each observed poi…
Mathematical Morphology for Color Images: An Image-Dependent Approach
This paper proposes one possibility to generalize the morphological operations (particularly, dilation, erosion, opening, and closing) to color images. First, properties of a desirable generalization are stated and a brief review is done on former approaches. Then, the method is explained, which is based on a total ordering of the colors in an image induced by its color histogram; this is valid for just one image and may present problems in smoothly coloured images. To solve these drawbacks a refinement consisting of smoothing the histogram and using a joint histogram of several images is presented. Results of applying the so-defined morphological operations on several sets of images are sh…
Girls4STEM: Gender Diversity in STEM for a Sustainable Future
Science, Technology, Engineering, and Mathematics (STEM) are key disciplines towards tackling the challenges related to the Sustainable Development Goals. However, evidence shows that women are enrolling in these disciplines in a smaller percentage than men, especially in Engineering related fields. As stated by the United Nations Women section, increasing the number of women studying and working in STEM fields is fundamental towards achieving better solutions to the global challenges, since the potential for innovation is larger. In this paper, we present the Girls4STEM project, which started in 2019 at the Escola Tè
Modeling of female human body shapes for apparel design based on cross mean sets
This paper is concerned with a method to build prototypes of human bodies that can be used for apparel design. One of the most important issues in the apparel development process is to define a sizing system to provide a good fitting for the majority of the population. Since anthropometric measures do not present the same linear growth with size in each dimension, it is very important to find a prototype that represents as accurately as possible each class in the sizing system. In this paper we propose a method based on the concept of random compact mean set to define prototypes in apparel design. From a cloud of 3D points obtained with a 3D scanner a solid that represents the human body is…
Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression
[EN] Purpose: The development of automatic and reliable algorithms for the detection and segmentation of the vertebrae are of great importance prior to any diagnostic task. However, an important problem found to accurately segment the vertebrae is the presence of the ribs in the thoracic region. To overcome this problem, a probabilistic atlas of the spine has been developed dealing with the proximity of other structures, with a special focus on ribs suppression. Methods: The data sets used consist of Computed Tomography images corresponding to 21 patients suffering from spinal metastases. Two methods have been combined to obtain the final result: firstly, an initial segmentation is performe…
Adaptive Bias Field Correction: Application on Abdominal MR Images
Segmentation of medical images is one of the most important phases for disease diagnosis. Accuracy, robustness and stability of the results obtained by image segmentation is a major concern. Many segmentation methods rely on absolute values of intensity level, which are affected by a bias term due to in-homogeneous field in magnetic resonance images. The main objective of this paper is two folded: (1) To show efficiency of an energy minimization based approach, which uses intrinsic component optimization, on abdominal magnetic resonance images. (2) To propose an adaptive method to stop the optimization automatically. The proposed method can control the value of the energy functional and sto…
Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap
This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, so…
Probabilistic liver atlas construction
Background Anatomical atlases are 3D volumes or shapes representing an organ or structure of the human body. They contain either the prototypical shape of the object of interest together with other shapes representing its statistical variations (statistical atlas) or a probability map of belonging to the object (probabilistic atlas). Probabilistic atlases are mostly built with simple estimations only involving the data at each spatial location. Results A new method for probabilistic atlas construction that uses a generalized linear model is proposed. This method aims to improve the estimation of the probability to be covered by the liver. Furthermore, all methods to build an atlas involve p…
Means of 2D and 3D Shapes and Their Application in Anatomical Atlas Building
This works deals with the concept of mean when applied to 2D or 3D shapes and with its applicability to the construction of digital atlases to be used in digital anatomy. Unlike numerical data, there are several possible definitions of the mean of a shape distribution and procedures for its estimation from a sample of shapes. Most popular definitions are based in the distance function or in the coverage function, each with its strengths and limitations. Closely related to the concept of mean shape is the concept of atlas, here understood as a probability or membership map that tells how likely is that a point belongs to a shape drawn from the shape distribution at hand. We devise a procedur…
Review on Machine Learning Based Lesion Segmentation Methods from Brain MR Images
Brain lesions are life threatening diseases. Traditional diagnosis of brain lesions is performed visually by neuro-radiologists. Nowadays, advanced technologies and the progress in magnetic resonance imaging provide computer aided diagnosis using automated methods that can detect and segment abnormal regions from different medical images. Among several techniques, machine learning based methods are flexible and efficient. Therefore, in this paper, we present a review on techniques applied for detection and segmentation of brain lesions from magnetic resonance images with supervised and unsupervised machine learning techniques.
Iteratively Learning a Liver Segmentation Using Probabilistic Atlases: Preliminary Results
This works deals with the concept of liver segmentation by using a priori information based on probabilistic atlases and segmentation learning based of previous steps. A probabilistic atlas is here understood as a probability or membership map that tells how likely is that a point belongs to a shape drawn from the shape distribution at hand. We devise a procedure to segment Perfusion Magnetic Resonance liver images that combines both: a probabilistic atlas of the liver and a segmentation algorithm based on global information of previous simpler segmentation steps, local information from close segmented slices and finally a mathematical morphology procedure, namely viscous reconstruction, to…