Selective image segmentation driven by region, edge and saliency functions.

Present active contour methods often struggle with the segmentation of regions displaying variations in texture, color, or intensity a phenomenon referred to as inhomogeneities. These limitation impairs their ability to precisely distinguish and outline diverse components within an image. Further some of these methods employ intricate mathematical formulations for energy minimization. Such complexity introduces computational sluggishness, making these methods unsuitable for tasks requiring real-time processing or rapid segmentation. Moreover, these methods are susceptible to being trapped in energy configurations corresponding to local minimum points. Consequently, the segmentation process fails to converge to the desired outcome. Additionally, the efficacy of these methods diminishes when confronted with regions exhibiting weak or subtle boundaries. To address these limitations comprehensively, our proposed approach introduces a fresh paradigm for image segmentation through the synchronization of region-based, edge-based, and saliency-based segmentation techniques. Initially, we adapt an intensity edge term based on the zero crossing feature detector (ZCD), which is used to highlight significant edges of an image. Secondly, a saliency function is formulated to detect salient regions from an image. We have also included a globally tuned region based SPF (signed pressure force) term to move contour away and capture homogeneous regions. ZCD, saliency and global SPF are jointly incorporated with some scaled value for the level set evolution to develop an effective image segmentation model. In addition, proposed method is capable to perform selective object segmentation, which enables us to choose any single or multiple objects inside an image. Saliency function and ZCD detector are considered feature enhancement tools, which are used to get important features of an image, so this method has a solid capacity to segment nature images (homogeneous or inhomogeneous) precisely. Finally, the adaption of the Gaussian kernel removes the need of any penalization term for level set reinitialization. Experimental results will exhibit the efficiency of the proposed method.

Self-initialized active contours for microscopic cell image segmentation.

Level set models are suitable for processing topological changes in different regions of images while performing segmentation. Active contour models require an empirical setting for initial parameters, which is tedious for the end-user. This study proposes an incremental level set model with the automatic initialization of contours based on local and global fitting energies that enable it to capture image regions containing intensity corruption or other light artifacts. The region-based area and the region-based length terms use signed pressure force (SPF) to strengthen the balloon force. SPF helps to achieve a smooth version of the gradient descent flow in terms of energy minimization. The proposed model is tested on multiple synthetic and real images. Our model has four advantages: first, there is no need for the end user to initialize the parameters; instead, the model is self-initialized. Second, it is more accurate than other methods. Third, it shows lower computational complexity. Fourth, it does not depend on the starting position of the contour. Finally, we evaluated the performance of our model on microscopic cell images (Coelho et al., in: 2009 IEEE international symposium on biomedical imaging: from nano to macro, IEEE, 2009) to confirm that its performance is superior to that of other state-of-the-art models.

Effects of transcranial magnetic stimulation on neurobiological changes in Alzheimer's disease (Review).

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline and brain neuronal loss. A pioneering field of research in AD is brain stimulation via electromagnetic fields (EMFs), which may produce clinical benefits. Noninvasive brain stimulation techniques, such as transcranial magnetic stimulation (TMS), have been developed to treat neurological and psychiatric disorders. The purpose of the present review is to identify neurobiological changes, including inflammatory, neurodegenerative, apoptotic, neuroprotective and genetic changes, which are associated with repetitive TMS (rTMS) treatment in patients with AD. Furthermore, it aims to evaluate the effect of TMS treatment in patients with AD and to identify the associated mechanisms. The present review highlights the changes in inflammatory and apoptotic mechanisms, mitochondrial enzymatic activities, and modulation of gene expression (microRNA expression profiles) associated with rTMS or sham procedures. At the molecular level, it has been suggested that EMFs generated by TMS may affect the cell redox status and amyloidogenic processes. TMS may also modulate gene expression by acting on both transcriptional and post­transcriptional regulatory mechanisms. TMS may increase brain cortical excitability, induce specific potentiation phenomena, and promote synaptic plasticity and recovery of impaired functions; thus, it may re­establish cognitive performance in patients with AD.

Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours.

Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH2 databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed. Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.

SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases.

Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of state-of-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19.

Rehabilitation of a patient with spinal cord decompression sickness: First case report from Saudi Arabia.

This case brings attention to development of rehabilitation protocols for patients with decompression sickness (DCS). A lack of data regarding DCS renders the need of conducting multicenter studies to document the epidemiology and outcomes of spinal cord DCS in Saudi Arabia.

Functional outcomes in geriatric patients with spinal cord injuries at a tertiary care rehabilitation hospital in Saudi Arabia.

STUDY DESIGN: Retrospective study. OBJECTIVE: To identify demographic features, clinical characteristics, and complications associated with spinal cord injuries/disorders (SCI/D) among elderly individuals at a rehabilitation hospital and to measure the functional outcomes of rehabilitation. SETTING: Rehabilitation hospital in King Fahad Medical City (KFMC), Riyadh, Saudi Arabia. METHODS: The study was conducted in elderly individuals (aged ≥65 years) with SCI/D, admitted to an inpatient rehabilitation program between October 2014 and 2015. Demographic and clinical data were recorded along with functional independence measure (FIM) score at admission (FIMa) and discharge (FIMd). Data were descriptively analyzed. Association of non-metric and metric variables with complications was measured using χ2, and Student's t-test, respectively. RESULTS: Twenty-four individuals with SCI/D (95.8% were male and retired) with mean (standard deviation, SD) age of 72.3 (6.3) years were included. The most common co-morbidities were hypertension (75.0%), and diabetes mellitus (58.3%). Degenerative cervical myelopathy (33.3%) was the most common cause of SCD. Of all, nine (37.5%) individuals had clinical complications (urinary tract infection(UTI); 8/9, surgical wound infection; 1/9). Mean (SD) hospitalization period during inpatient rehabilitation was 66.0 (13.9) days. Mean (SD) FIMa scores improved from 71.7 (17.3) to 85.3 (16.8) at discharge. Co-morbidities associated with complications were peripheral vascular disease, ischemic heart disease, and stroke. CONCLUSION: In Saudi Arabia, non-traumatic spinal etiologies are the most frequent cause of spinal cord dysfunction in the elderly. Male gender, hypertension, and diabetes mellitus were high-risk factors among the geriatric age group with SCI/D. Elderly individuals with SCI/D without complications can have a shorter hospitalization period and higher functional gains during rehabilitation.