Heuristically improved Bayesian segmentation of brain MR images

One of the major tasks or even the most prevalent task in medical imageprocessing is image segmentation. Among them, brain MR images sufferfrom some difficulties such as intensity inhomogeneity of tissues, partialvolume effect, noise and some other imaging artifacts and so their segmentation is more challenging. Therefore, brain MRI segmentationbased on just gray values is prone to error. Hence involving problem specific heuristics and expert knowledge in designing segmentation algorithms seems to be useful. A two-phase segmentation algorithm basedon Bayesian method is proposed in this paper. The Bayesian part uses thegray value in segmenting images and the segmented image is used as theinput to the second phase to improve the misclassified pixels especially inborders between tissues. Similarity index is used to compare our algorithmwith the well known method of Ashburner which has been implemented inStatistical Parametric Mapping (SPM) package. Brainweb as a simulatedbrain MRI dataset is used in evaluating the proposed algorithm. Resultsshow that our algorithm performs well in comparison with the one implemented in SPM. It can be concluded that incorporating expert knowledge and problem specific heuristics improve segmentation result.The major advantage of proposed method is that one can update theknowledge base and incorporate new information into segmentationprocess by adding new rules.Keywords: Magnetic Resonance Imaging (MRI); Segmentation; Bayesianclassifier; Heuristic

Spatial based Expectation Maximizing (EM)

<p>Abstract</p> <p>Background</p> <p>Expectation maximizing (EM) is one of the common approaches for image segmentation.</p> <p>Methods</p> <p>an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM.</p> <p>Results</p> <p>the findings show that the proposed algorithm produces higher similarity index.</p> <p>Conclusions</p> <p>experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.</p

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

Spatial fuzzy c-mean sobel algorithm with grey wolf optimizer for MRI brain image segmentation

Segmentation is the process of dividing the original image into multiple sub regions called segments in such a way that there is no intersection between any two regions. In medical images, the segmentation is hard to obtain due to the intensity similarity among various regions and the presence of noise in medical images. One of the most popular segmentation algorithms is Spatial Fuzzy C-means (SFCM). Although this algorithm has a good performance in medical images, it suffers from two issues. The first problem is lack of a proper strategy for point initialization step, which must be performed either randomly or manually by human. The second problem of SFCM is having inaccurate segmented edges. The goal of this research is to propose a robust medical image segmentation algorithm that overcomes these weaknesses of SFCM for segmenting magnetic resonance imaging (MRI) brain images with less human intervention. First, in order to find the optimum initial points, a histogram based algorithm in conjunction with Grey Wolf Optimizer (H-GWO) is proposed. The proposed H-GWO algorithm finds the approximate initial point values by the proposed histogram based method and then by taking advantage of GWO, which is a soft computing method, the optimum initial values are found. Second, in order to enhance SFCM segmentation process and achieve higher accurate segmented edges, an edge detection algorithm called Sobel was utilized. Therefore, the proposed hybrid SFCM-Sobel algorithm first finds the edges of the original image by Sobel edge detector algorithm and finally extends the edges of SFCM segmented images to the edges that are detected by Sobel. In order to have a robust segmentation algorithm with less human intervention, the H-GWO and SFCM-Sobel segmentation algorithms are integrated to have a semi-automatic robust segmentation algorithm. The results of the proposed H-GWO algorithms show that optimum initial points are achieved and the segmented images of the SFCM-Sobel algorithm have more accurate edges as compared to recent algorithms. Overall, quantitative analysis indicates that better segmentation accuracy is obtained. Therefore, this algorithm can be utilized to capture more accurate segmented in images in the era of medical imaging

Knee cartilage segmentation using multi purpose interactive approach

Interactive model incorporates expert interpretation and automated segmentation. However, cartilage has complicated structure, indistinctive tissue contrast in magnetic resonance image of knee hardens image review and existing interactive methods are sensitive to various technical problems such as bi-label segmentation problem, shortcut problem and sensitive to image noise. Moreover, redundancy issue caused by non-cartilage labelling has never been tackled. Therefore, Bi-Bezier Curve Contrast Enhancement is developed to improve visual quality of magnetic resonance image by considering brightness preservation and contrast enhancement control. Then, Multipurpose Interactive Tool is developed to handle users’ interaction through Label Insertion Point approach. Approximate NonCartilage Labelling system is developed to generate computerized non-cartilage label, while preserves cartilage for expert labelling. Both computerized and interactive labels initialize Random Walks based segmentation model. To evaluate contrast enhancement techniques, Measure of Enhancement (EME), Absolute Mean Brightness Error (AMBE) and Feature Similarity Index (FSIM) are used. The results suggest that Bi-Bezier Curve Contrast Enhancement outperforms existing methods in terms of contrast enhancement control (EME = 41.44±1.06), brightness distortion (AMBE = 14.02±1.29) and image quality (FSIM = 0.92±0.02). Besides, implementation of Approximate Non-Cartilage Labelling model has demonstrated significant efficiency improvement in segmenting normal cartilage (61s±8s, P = 3.52 x 10-5) and diseased cartilage (56s±16s, P = 1.4 x 10-4). Finally, the proposed labelling model has high Dice values (Normal: 0.94±0.022, P = 1.03 x 10-9; Abnormal: 0.92±0.051, P = 4.94 x 10-6) and is found to be beneficial to interactive model (+0.12)

ASSESSMENT OF GLIOMA RESPONSE TO 8 GY RADIOTHERAPY ON MULTIPLE MRI BIOMARKERS BY APPLYING IMAGE SEGMENTATION ALGORITHM

Magnetic Resonance Imaging (MRI) is playing a significant role in assessment of treatment response for a variety of diseases. To investigate multi-parametric MRI biomarkers for the assessment of glioma response to radiotherapy, we made comparison among different MRI parameters. In the tumor extraction step, we compare the manual segmentation, automatic and semi-automatic methods based on region of interest (ROI) results. In our experiments, thirteen nude rats injected with U87 tumor were irradiated by 8 Gy radiation dose. All MRI were performed on a 4.7 T animal scanner at time points of pre-radiation, 1 day, 4 days and 8 days post-radiation. Multi-parametric MRI signals of the tumors were compared in quantitative. Two experts performed manual and semi-automatic methods on tumor extraction on Amide Proton Transfer-weighted (APTw) maps. The results shows that average Apparent Diffusion Coefficient (ADC) intensity of ROI had a great increase during the post-radiation. The relative blood flow values (tumor vs. normal contralateral side of brain) had a continuous decrease after radiotherapy. Similarly, APTw signals intensity decreased at all time after radiotherapy. Semi-automatic method gave a more stable ROI extraction result on APTw maps than manual segmentation without rater dependence, and with less time consumption. In conclusion, ADC, blood flow, APTw are all helpful signals in assessing glioma response to radiotherapy. Also, semi-automatic method on ROI extraction showed higher efficiency and stability than manual method

Computational Imaging Biomarkers For Precision Medicine: Characterizing Heterogeneity In Breast Cancer

In the United States, 1 in 8 women are diagnosed with breast cancer. Breast tumor heterogeneity is well-established, with intratumor heterogeneity manifesting spatially and temporally. Increased heterogeneity is associated with adverse clinical outcomes. Current critical disease treatment decisions are made on the basis of biomarkers acquired from tissue samples, largely under sampling the heterogeneous disease burden. In order to drive precision medicine treatment strategies for cancer, personalized biomarkers are needed to truly characterize intratumor heterogeneity. Medical imaging can provide anon-invasive, whole tumor sampling of disease burden at the time of diagnosis and allows for longitudinal monitoring of disease progression. The studies outlined in this thesis introduce analytical tools developed through computer vision, bioinformatics, and machine learning and use diagnostic and longitudinal clinical images of breast cancer to develop computational imaging biomarkers characterizing intratumor heterogeneity. Intrinsic imaging phenotypes of spatial heterogeneity, identified in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images at the time of diagnosis, were identified and validated, demonstrating improved prognostic value over conventional histopathologic biomarkers when predicting 10-year recurrence free survival. Intrinsic phenotypes of longitudinal change in spatial heterogeneity in response to neoadjuvant treatment, identified in DCE-MRI were identified and leveraged as prognostic and predictive biomarkers, demonstrating augmented prognostic value when added to conventional histopathologic and personalized molecular biomarkers. To better characterize 4-D spatial and temporal heterogeneity, illuminated through dynamic positron emission tomography imaging, a novel 4-D segmentation algorithm was developed to identify spatially constrained, functionally discrete intratumor sub-regions. Quantifying the identified sub-regions through a novel imaging signature demonstrated the prognostic value of characterizing intratumor heterogeneity when predicting recurrence free survival, demonstrating prognostic improvement over established histopathologic biomarkers and conventional kinetic model derived parameters. Collectively, the studies in this thesis demonstrate the value of leveraging computational imaging biomarkers to characterize intratumor heterogeneity. Such biomarkers have the potential to be utilized towards precision medicine for cancer care

Segmentação de imagens fetais com potencial para desenvolvimento de ferramentas de apoio ao diagnóstico

Programa Doutoral em Engenharia Eletrónica e de ComputadoresDurante uma gravidez é aconselhável a realização de 3 exames ecográficos. O primeiro, e reconhecido pelos especialistas como mais importante, é o do primeiro trimestre. Neste exame, realizado entre as 11 e as 14 semanas, é possível avaliar a idade gestacional, o desenvolvimento fetal e, mais importante, as anomalias fetais. Na avaliação das anomalias fetais incluem-se as cromossómicas, que são detetáveis a partir da observação da medida da Translucência da Nuca mas que deve ser cruzada com a medida da Distância Crânio-Caudal e a idade materna. As medidas são retiradas manualmente e os seus valores variam com a disponibilidade física e a motivação do operador, pelo que os resultados mostram variabilidade intra e inter-operador. As imagens recolhidas pelos sistemas de aquisição baseados em ultrassons apresentam pouco detalhe, baixo contraste, baixa relação sinal/ruído e grande variabilidade morfológica que dificulta a tarefa de segmentação e, consequentemente, o desenvolvimento de sistemas de medição automáticos. Como tal, o seu tratamento exige a utilização de técnicas que reúnam características adequadas e que permitam o desenvolvimento de sistemas robustos. Este trabalho trata a questão da extração automática da medida da Distância Crânio-Caudal (DCC) a partir das imagens de ultrassons habitualmente usadas para este fim. Para tal, propõe a utilização de técnicas de Fuzzy Clustering, de Contornos Ativos e de Aprendizagem Máquina, nomeadamente SVMs, para a segmentação das imagens com vista à identificação do corpo do feto. Estas abordagens potenciaram a formulação de novos modelos que permitem enfrentar as dificuldades inerentes ao tratamento deste tipo de imagens. São também propostas metodologias automáticas de extração da medida DCC, sendo que algumas delas dependem dos processos de segmentação sugeridos. Os resultados obtidos para a medida da DCC apresentam um erro absoluto médio relativo dentro dos intervalos de variabilidade inter-operador referidos na literatura.During pregnancy it is advisable to conduct 3 ultrasound examinations. The first and most important is performed in the first trimester. In this exam, done between the 11th and 14th week, the gestational age, the fetal development and, most importantly, the fetal abnormalities can be assessed. The assessment of fetal anomalies include chromosomal, which are detectable from observation measuring the nuchal translucency size. However it should be crossed with a measure of the crown-rump length and the maternal age. These measures are manually performed and their values vary with the physical availability and motivation of the operator, so the results show intra and inter-operator variability. The images collected by acquisition systems based on ultrasounds have little detail, low contrast, low signal/noise ratio and great morphological variability which difficult the segmentation task and the development of automatic measuring systems. Because of these reasons, ultrasound image processing requires the use of techniques that meet appropriate characteristics and that enable the development of robust systems. This work treats the subject of automatic extraction of the crown-rump length from ultrasound images commonly used for this purpose. It uses Fuzzy Clustering, Active Contours and Machine Learning techniques for the segmentation of images in order to identify the fetal body. These approaches promoted the development of new models that allow face the inherent difficulties in treating this type of images. Methods for the crown-rump length automatic measurement are also proposed, some of which depend on the suggested segmentation methods. The results obtained for the crown-rump length presented a relative mean absolute error within inter-operator variability ranges reported in the literature