Ischemic Stroke Detection System with a Computer-Aided Diagnostic Ability Using an Unsupervised Feature Perception Enhancement Method

We propose an ischemic stroke detection system with a computer-aided diagnostic ability using a four-step unsupervised feature perception enhancement method. In the first step, known as preprocessing, we use a cubic curve contrast enhancement method to enhance image contrast. In the second step, we use a series of methods to extract the brain tissue image area identified during preprocessing. To detect abnormal regions in the brain images, we propose using an unsupervised region growing algorithm to segment the brain tissue area. The brain is centered on a horizontal line and the white matter of the brain’s inner ring is split into eight regions. In the third step, we use a coinciding regional location method to find the hybrid area of locations where a stroke may have occurred in each cerebral hemisphere. Finally, we make corrections and mark the stroke area with red color. In the experiment, we tested the system on 90 computed tomography (CT) images from 26 patients, and, with the assistance of two radiologists, we proved that our proposed system has computer-aided diagnostic capabilities. Our results show an increased stroke diagnosis sensitivity of 83% in comparison to 31% when radiologists use conventional diagnostic images

Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy in extraction of infarct lesion. Still, limited works have been reported in isolating the penumbra tissues and infarct core separately. The segmentation of the penumbra tissues is necessary because that region has the potential to recover. This paper presented an automated segmentation algorithm on diffusion-weighted magnetic resonance imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering techniques. A greyscale image contains only intensity information and often misdiagnosed due to overlap intensity of an image. Colourization is the method of adding colours to greyscale images which allocate luminance or intensity for red, green, and blue channels. The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. The result shows the potential of automated segmentation to differentiate between the healthy and lesion tissues with 90.08% in accuracy and 0.89 in dice coefficient. The development of an automated segmentation algorithm was successfully achieved by entirely depending on the computer with minimal interaction

A Review of MRI Acute Ischemic Stroke Lesion Segmentation

Immediate treatment of a stroke can minimize long-term effects and even help reduce death risk. In the ischemic stroke cases, there are two zones of injury which are ischemic core and ischemic penumbra zone. The ischemic penumbra indicates the part that is located around the infarct core that is at risk of developing a brain infarction. Recently, various segmentation methods of infarct lesion from the MRI input images were developed and these methods gave a high accuracy in the extraction and detection of the infarct core. However, only some limited works have been reported to isolate the penumbra tissues and infarct core separately. The challenges exist in ischemic core identification are traditional approach prone to error, time-consuming and tedious for medical expert which could delay the treatment. In this paper, we study and analyse the segmentation algorithms for brain MRI ischemic of different categories. The focus of the review is mainly on the segmentation algorithms of infarct core with penumbra and infarct core only. We highlight the advantages and limitations alongside the discussion of the capabilities of these segmentation algorithms and its key challenges. The paper also devised a generic structure for automated stroke lesion segmentation. The performance of these algorithms was investigated by comparing different parameters of the surveyed algorithms. In addition, a new structure of the segmentation process for segmentation of penumbra is proposed by considering the challenges remains. The best accuracy for segmentation of infarct core and penumbra tissues is 82.1% whereas 99.1% for segmentation infarct core only. Meanwhile, the shortest average computational time recorded was 3.42 seconds for segmenting 10 slices of MR images. This paper presents an inclusive analysis of the discussed papers based on different categories of the segmentation algorithm. The proposed structure is important to enable a more robust and accurate assessment in clinical practice. This could be an opportunity for the medical and engineering sector to work together in designing a complete end-to-end automatic framework in detecting stroke lesion and penumbra

Avalia??o de Acidente Vascular Cerebral em Tomografia Computadorizada Utilizando Algoritmo de Otimiza??o de Formigas

O acidente vascular cerebral (AVC) ? uma das maiores causas de morte e de incapacidades neurol?gicas do mundo, sendo a doen?a neurol?gica mais comum e potencialmente mais devastadora, e por essa raz?o ? respons?vel por um grande n?mero de pesquisas e inova??es na ?rea de imagens m?dicas. No Brasil h? uma distribui??o extremamente desigual de recursos m?dicos de boa qualidade em decorr?ncia de sua grande extens?o territorial. Dessa forma, existem in?meros locais e servi?os de sa?de em que n?o h? a presen?a de um especialista em radiologia para observar as imagens de tomografia computadorizada (TC). Por essa raz?o h? uma motiva??o para o desenvolvimento de sistemas computadorizados para o aux?lio ao diagn?stico de doen?as utilizando t?cnicas de processamento de imagens. T?cnicas de processamento digital de imagens podem ser utilizadas para auxiliar o diagn?stico m?dico dessa patologia, possibilitando um diagn?stico mais r?pido, bem como um acompanhamento da ?rea de extens?o das les?es isqu?micas e hemorr?gicas causadas pelo AVCi (isqu?mico) ou AVCh (hemorr?gico). Ent?o, os algoritmos desenvolvidos para detec??o de AVC poderiam ser utilizados para auxiliar cl?nicos, ou outros profissionais de sa?de, para que esses possam ou encaminhar para algum centro especializado pr?ximo ou iniciar o tratamento adequado o mais r?pido poss?vel melhorando o progn?stico dos pacientes acometidos pela patologia. Neste trabalho foram desenvolvidos e implementados cinco algoritmos para detectar e real?ar as ?reas de AVCi e AVCh em imagens de TC de cr?nio, dos quais tr?s foram utilizados para detec??o de AVCi agudo/subagudo (nos est?gios iniciais) e dois para detec??o de AVCh. Inicialmente, foram implementados os algoritmos para a detec??o dessas duas patologias baseados em limiariza??o, e em seguida foi implementado o algoritmo de segmenta??o de imagens baseado em ACO (Ant Colony Optimization) e k-means. Baseado nessa segmenta??o com ACO foi desenvolvido um algoritmo de detec??o de AVCh, um algoritmo de detec??o dos ventr?culos cerebrais e posterior detec??o do AVCi utilizando a limiariza??o e um algoritmo de detec??o de AVCi agudo/subagudo. Em seguida, foram calculados e analisados os resultados estat?sticos para cada um dos algoritmos implementados, analisando a detec??o por paciente, por cortes e por pixels. Assim, sendo realizada uma avalia??o da detec??o dos dois tipos de AVC em rela??o a cada um dos algoritmos desenvolvidos. Os melhores resultados obtidos para a detec??o do AVCh foram com o algoritmo de segmenta??o baseado no ACO que apresenta uma sensibilidade, uma especificidade e uma acur?cia na detec??o por paciente de 100%, por corte apresenta uma sensibilidade de 51%, uma especificidade de 100% e uma acur?cia de 99%, e por pixel possui uma sensibilidade de 34%, uma especificidade de 99% e uma acur?cia de 99%. O processamento do conjunto das 22 imagens de cada paciente foi realizado em 1 minuto e 15 segundos por esse algoritmo. De forma semelhante, os melhores resultados para a detec??o do AVCi foram obtidos com o algoritmo ACO para a detec??o da ?rea de isquemia, que apresenta uma sensibilidade de 72%, uma especificidade de 88% e uma acur?cia na detec??o por paciente de 88%, por corte apresenta uma sensibilidade de 27%, uma especificidade de 98% e uma acur?cia de 98%, e por pixel possui uma sensibilidade de 12%, uma especificidade de 99% e uma acur?cia de 99%. Esse algoritmo possui um tempo de processamento para o conjunto de 20 imagens de um paciente de 1 minuto e 5 segundos.CAPE