Adaptive filter with Riemannian manifold constraint.

The adaptive filtering theory has been extensively developed, and most of the proposed algorithms work under the assumption of Euclidean space. However, in many applications, the data to be processed comes from a non-linear manifold. In this article, we propose an alternative adaptive filter that works on a manifold, thus generalizing the filtering task to non-Euclidean spaces. To this end, we generalized the least-mean-squared algorithm to work on a manifold using an exponential map. Our experiments showed that the proposed method outperforms other state-of-the-art algorithms in several filtering tasks.

Computer-aided classification of small airways dysfunction using impulse oscillometric features: a children-focused review.

Background and objective Spirometry, which is the most commonly used technique for asthma diagnosis, is often unsuitable for small children as it requires them to follow exact instructions and perform extreme inspiration and expiration maneuvers. In contrast, impulse oscillometry (IOS) is a child-friendly technique that could serve as an alternative pulmonary function test (PFT) for asthma diagnosis and control in children as it offers several advantages over spirometry. However, the complex test results of IOS may be difficult to be understood by practitioners due to its reliance on mechanical and electrical models of the human pulmonary system. Recognizing this reality, computer-aided decision systems could help to improve the utility of IOS. The main objective of this paper is to understand the current computer-aided classification research works on this topic. Methods This paper presents a methodological review of research works related to the computer-aided classification of peripheral airway obstruction using the IOS technique, which is focused on, but not limited to, asthmatic children. Publications that focused on computer-aided classification of asthma, peripheral dysfunction and/or small airway impairment (SAI) based on impulse oscillometric features were selected for this review. Results Out of the 34 articles that were identified using the selected scientific web databases and topic-related parameters, only eight met the eligibility criteria. The most relevant results of the articles reviewed are related to the performance of the different classifiers using static features which are solely based on the first pulmonary function testing measurements (IOS and spirometry). These results included an overall classifiers' accuracy performance ranging from 42.24% to 98.61%. Conclusion There is still a great opportunity to improve the utility of IOS by developing more computer-aided robust classifiers, specifically for the asthmatic children population as the classification studies performed to date (1) are limited in number, (2) include features derived from tests that are not optimally suitable for children, (3) are solely bi-class (mostly asthma and non-asthma) and therefore fail to include different degrees of peripheral obstruction for disease prevention and control and (4) lack of validation in cases that focus on multi-class classification of the different degrees of peripheral airway obstruction.

Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature.

Positron emission tomography (PET) provides images of metabolic activity in the body, and it is used in the research, monitoring, and diagnosis of several diseases. However, the raw data produced by the scanner are severely corrupted by noise, causing a degraded quality in the reconstructed images. In this paper, we proposed a reconstruction algorithm to improve the image reconstruction process, addressing the problem from a variational geometric perspective. We proposed using the weighted Gaussian curvature (WGC) as a regularization term to better deal with noise and preserve the original geometry of the image, such as the lesion structure. In other contexts, the WGC term has been found to have excellent capabilities for preserving borders and structures of low gradient magnitude, such as ramp-like structures; at the same time, it effectively removes noise in the image. We presented several experiments aimed at evaluating contrast and lesion detectability in the reconstructed images. The results for simulated images and real data showed that our proposed algorithm effectively preserves lesions and removes noise.

State of the art survey on MRI brain tumor segmentation.

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.