A Genetic Algorithm Based Feature Selection for Classification of Brain MRI Scan Images Using Random Forest Classifier

A brain tumour is a mass of tissue that is formed by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Magnetic Resonance Imaging is a useful imaging technique that is widely used by physicians to investigate different pathologies. After a long clinical research, it is proved to be harmless. Improvement in computing power has introduced Computer Aided Diagnosis (CAD) which can efficiently work in an automated environment. Diagnosis or classification accuracy of such a CAD system is associated with the selection of features. This paper proposes an enhanced brain MRI image classifier targeting two main objectives, the first is to achieve maximum classification accuracy and second is to minimize the number of features for classification. Feature selection is performed using Genetic Algorithm (GA) while classifiers used are Random forest Classifier

Comparison of T1-maps and late gadolinium enhancement images in the detection of Myocardial Fibrosis in Hypertrophic Cardiomyopathy

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica, 2021, Universidade de Lisboa, Faculdade de CiênciasHypertrophic Cardiomyopathy (HCM) is characterized as an abnormal and heterogeneous thickening of the Left Ventricle (LV) wall. HCM is the leading cause of sudden cardiac death in children and young people, with an estimated prevalence of 1:500 in the general population. Myocardial fibrosis is the key histopathological hallmark in HCM and is presented in different patterns: interstitial diffuse fibrosis which, if not treated, evolves to replacement fibrosis. Cardiac Magnetic Resonance (CMR) imaging has been used for the detection and quantification of myocardial fibrosis. The Late Gadolinium Enhancement (LGE) technique is the primary tool for non-invasive tissue characterization, particularly for replacement fibrosis. Conversely, T1 mapping is commonly used for the detection of diffuse interstitial fibrosis, frequently missed using LGE. The clear disadvantage of LGE relies on the need to inject contrast agents that, despite being considered safe, may accumulate in the body for years and potentially cause nephrogenic systemic fibrosis in end-stage chronic kidney disease patients. The capability of native T1 mapping identifying not only diffuse interstitial but also replacement fibrosis would play a pivotal role in HCM diagnosis. The potential of native T1 mapping for a cheaper and non-contrast HCM assessment needs to be further studied. A database of 15 HCM patients, without and with fibrosis, was acquired at Hospital da Luz, Lisboa. In this project, (1) an extensive image preprocessing pipeline was applied to aim for the best possible spatial alignment of the myocardium between the two modalities (native T1 mapping and LGE); (2) the mean native T1 values of individuals without and with the presence of scarred tissue were examined; (3) a pixel-by-pixel analysis was performed to investigate if there is a correlation between fibrotic tissue in LGE and hyperintense regions in native T1 mapping; (4) a Texture Analysis (TA) was performed to study if texture information of native T1 mapping could provide differential diagnosis or prognostic information beyond mean T1 values. The first step was the most longstanding and challenging process. The registration of T1 and LGE images is difficult due to the different intensity profiles. The registration of the myocardial masks using a model with rigid, affine, and free-form deformation transformations revealed to be the best methodology. Mean native T1 values were not increased in patients with scarred tissue. Regarding the third aim, no clear intensity correlation between techniques was observed, which suggests the need for the TA. Seven features (in a total of 350) were selected to distinguish between cardiac segments without and with fibrotic tissue using a ML (Machine Learning) algorithm that finds the features that most contribute to distinguish the two groups. Four first-order features distinguish the cohorts due to the presence of scarred tissue - hyperintense zones - and three texture features suggest that the fibrotic remodeling in the myocardium of HCM patients might be associated with a more heterogeneous tissue texture. A Receiver Operating Characteristics (ROC) analysis was performed and revealed that the Cluster Prominence is the feature that best distinguishes sections without and with fibrotic tissue (accuracy of 70%) but with low sensitivity (65%) and low specifity (64%). A model with the 90th Percentile feature revealed an accuracy of 64%, sensitivity of 71% and specificity of 57%. Studying the Variance feature, the achieved accuracy was 63%, with 66% of sensitivity and 60% of specificity. The remaining features yielded lower accuracy values than the ones previously mentioned, but all of them higher than 50%. The low sensitivity and specificity of the best three models suggest that analysing these values considering these features may help cardiologists to identify focal fibrosis regions and avoid contrast injection methods but may not provide an accurate diagnosis of the presence of fibrotic tissue alone. Further research on the correlation of native T1 mapping and LGE cardiac images is highly recommended to develop a contrast-agent-free technology to replace LGE.A Cardiomiopatia Hipertrófica (do inglês, HCM) é descrita por um espessamento anormal e heterogéneo da parede do ventrículo esquerdo (do inglês, LV). A HCM é a principal causa de morte súbita cardíaca em crianças e jovens, com uma prevalência estimada de 1:500 na população em geral. Esta doença é, na sua maioria, hereditária, e causada por variantes nos genes da proteína do sarcómero (predominantemente MYH7 e MYBPC3). A fibrose do miocárdio é a principal marca histopatológica da HCM e apresenta-se em diferentes padrões: fibrose intersticial difusa que, se não tratada, evolui para fibrose focal. A fibrose é caracterizada por um aumento da deposição de colagénio, que afeta a viabilidade do miocárdio. A imagem de Ressonância Magnética Cardíaca (do inglês, CMR) tem sido usada para a deteção e quantificação de fibrose do miocárdio. A técnica de Realce Tardio (do inglês, LGE) é a principal ferramenta para caracterização não invasiva de tecidos, particularmente de fibrose focal. Em contrapartida, o mapeamento T1 é a técnica mais utilizada para deteção de fibrose intersticial difusa, frequentemente não detetada usando LGE. A clara desvantagem do LGE reside na necessidade de injeção de agentes de contraste. Apesar destes agentes serem considerados seguros, frequentemente causam alergias, podem-se acumular no corpo, por anos, e podem causar fibrose sistémica nefrogénica em pacientes com doença renal crónica terminal. A capacidade do mapeamento T1 nativo identificar, não só a fibrose intersticial difusa mas também a fibrose focal, desempenharia um papel fundamental no diagnóstico da HCM. Consequentemente, é de extrema importância estudar o potencial do mapeamento T1 nativo para uma avaliação desta patologia sem contraste e, desta forma, eliminar os riscos associados à injeção de contraste e reduzir os custos e tempo de preparação associados à utilização de gadolínio. Uma base de dados de 15 pacientes com HCM, com e sem fibrose, previamente adquirida no Hospital da Luz, Lisboa, foi analisada. Neste projeto, (1) aplicou-se um extenso conjunto de passos de pré-processamento de imagem para alcançar a melhor técnica possível de alinhamento espacial do miocárdio entre as duas modalidades (mapeamento T1 nativo e Realce Tardio); (2) após a divisão do miocárdio em 6 secções, como sugerido pela American Heart Association, examinaram-se os valores médios de T1, para cada secção, de indivíduos sem e com presença de tecido cicatricial; (3) realizou-se uma análise pixel a pixel para investigar se existe uma correlação entre o tecido fibrótico em LGE e as regiões hiperintensas no mapeamento T1 nativo; (4) realizou-se uma análise de textura para estudar se a informação de textura do mapeamento T1 nativo poderia fornecer um diagnóstico diferencial ou informação prognóstica além dos valores médios de T1 nativo. A primeira etapa revelou ser o processo mais demorado e desafiante. O batimento cardíaco e o ciclo respiratório representam dois desafios no registo de imagens cardíacas. Para além dos comuns desafios em alinhamento de imagens cardíacas da mesma modalidade, alinhar imagens de diferentes modalidades torna-se um processo mais complexo. Em primeiro lugar, o registo de imagens T1 e de LGE é dificultado pelos distintos perfis de intensidade das duas modalidades. Em segundo lugar, a aquisição de imagens de Realce Tardio ocorre cerca de 7 minutos após a aquisição do mapeamento T1, e o movimento dos pacientes durante este intervalo de tempo é uma fonte adicional de erro. Diferentes softwares foram utilizados, e uma imagem sintética ponderada em T1 foi criada, com o intuito de apresentar intensidades mais similares à imagem a ser alinhada (imagem de LGE). O registo das máscaras miocárdicas por meio de um modelo com transformações rígida, afim e deformações livres mostrou ser a melhor metodologia a aplicar. Os valores médios de T1 nativo não aumentaram significativamente em pacientes com tecido cicatricial, apesar de haver um aumento dos valores de T1 nativo em determinadas secções, em cortes basais e intermédios. Relativamente ao terceiro objetivo abordado, não foi observada uma clara correlação de intensidades entre as técnicas, o que reforçou a necessidade de uma análise de textura (do inglês, TA). Esta análise revelou as sete melhores características (num total de 350) que distinguem segmentos cardíacos sem e com tecido fibrótico, aplicando um método de Machine Learning (do inglês, ML) que identificou, sequencialmente, as features que adicionavam mais informação ao modelo que distinguia os dois grupos de segmentos. Quatro características de primeira ordem distinguem os segmentos devido à presença de tecido cicatricial - zonas hiperintensas - e três características de textura sugerem que a remodelação fibrótica no miocárdio de pacientes com HCM pode estar associada a uma textura mais heterogénea. Foi implementada uma análise ao desempenho de modelos com as features selecionadas, que revelou que a Cluster Prominence é a característica que melhor distingue secções sem e com tecido fibrótico, apesar de com baixa sensibilidade (65%) e baixa especificidade (64%). Um modelo que analisa o Percentil 90 revelou uma precisão de 64%, sensibilidade de 71% e especificidade de 57%. No estudo da Variância, a precisão foi de 63%, a sensibilidade 66% e a especificidade 60%. As restantes features apresentaram valores de precisão inferiores aos mencionados mas acima de 50%. Um modelo com a combinação das sete features selecionadas não melhorou a performance do modelo (precisão de 62%, sensibilidade de 75% e 49% de especificidade). A baixa sensibilidade e especificidade sugerem que a análise desses valores nessas características pode ajudar os cardiologistas a identificar regiões focais de fibrose e evitar métodos de injeção de contraste, mas pode não fornecer um diagnóstico preciso da presença de tecido fibrótico por si só. Em futuras aquisições, encontrar valores semelhantes nas features acima mencionadas, principalmente na Cluster Prominence, em novos dados, poderia ajudar os cardiologistas a identificar regiões de fibrose focal. Desta forma, não seria necessário analisar imagens de Realce Tardio, o que se traduziria na eliminação de injeção de agentes de contraste. Pesquisas adicionais focadas na correlação do mapeamento T1 nativo e imagens cardíacas de LGE são de extrema importância para desenvolver uma tecnologia independente da injeção de agentes de contraste, que substitua o Realce Tardio

Template-Cut: A Pattern-Based Segmentation Paradigm

We present a scale-invariant, template-based segmentation paradigm that sets up a graph and performs a graph cut to separate an object from the background. Typically graph-based schemes distribute the nodes of the graph uniformly and equidistantly on the image, and use a regularizer to bias the cut towards a particular shape. The strategy of uniform and equidistant nodes does not allow the cut to prefer more complex structures, especially when areas of the object are indistinguishable from the background. We propose a solution by introducing the concept of a "template shape" of the target object in which the nodes are sampled non-uniformly and non-equidistantly on the image. We evaluate it on 2D-images where the object's textures and backgrounds are similar, and large areas of the object have the same gray level appearance as the background. We also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning purposes.Comment: 8 pages, 6 figures, 3 tables, 6 equations, 51 reference

Texture Analysis in Magnetic Resonance Imaging: Review and Considerations for Future Applications

Texture analysis is a technique used for the quantification of image texture. It has been successfully used in many fields, and in the past years it has been applied in magnetic resonance imaging (MRI) as a computer-aided diagnostic tool. Quantification of the intrinsic heterogeneity of different tissues and lesions is necessary as they are usually imperceptible to the human eye. In the present chapter, we describe texture analysis as a process consisting of six steps: MRI acquisition, region of interest (ROI) definition, ROI preprocessing, feature extraction, feature selection, and classification. There is a great variety of methods and techniques to be chosen at each step and all of them can somehow affect the outcome of the texture analysis application. We reviewed the literature regarding texture analysis in clinical MRI focusing on the important considerations to be taken at each step of the process in order to obtain maximum benefits and to avoid misleading results

TEXTURAL CLASSIFICATION OF MULTIPLE SCLEROSISLESIONS IN MULTIMODAL MRI VOLUMES

Background and objectives:Multiple Sclerosis is a common relapsing demyelinating diseasecausing the significant degradation of cognitive and motor skills and contributes towards areduced life expectancy of 5 to 10 years. The identification of Multiple Sclerosis Lesionsat early stages of a patient’s life can play a significant role in the diagnosis, treatment andprognosis for that individual. In recent years the process of disease detection has been aidedthrough the implementation of radiomic pipelines for texture extraction and classificationutilising Computer Vision and Machine Learning techniques. Eight Multiple Sclerosis Patient datasets have been supplied, each containing one standardclinical T2 MRI sequence and four diffusion-weighted sequences (T2, FA, ADC, AD, RD).This work proposes a Multimodal Multiple Sclerosis Lesion segmentation methodology util-ising supervised texture analysis, feature selection and classification. Three Machine Learningmodels were applied to Multimodal MRI data and tested using unseen patient datasets to eval-uate the classification performance of various extracted features, feature selection algorithmsand classifiers to MRI volumes uncommonly applied to MS Lesion detection. Method: First Order Statistics, Haralick Texture Features, Gray-Level Run-Lengths, His-togram of Oriented Gradients and Local Binary Patterns were extracted from MRI volumeswhich were minimally pre-processed using a skull stripping and background removal algorithm.mRMR and LASSO feature selection algorithms were applied to identify a subset of rankingsfor use in Machine Learning using Support Vector Machine, Random Forests and ExtremeLearning Machine classification. Results: ELM achieved a top slice classification accuracy of 85% while SVM achieved 79%and RF 78%. It was found that combining information from all MRI sequences increased theclassification performance when analysing unseen T2 scans in almost all cases. LASSO andmRMR feature selection methods failed to increase accuracy, and the highest-scoring groupof features were Haralick Texture Features, derived from Grey-Level Co-occurrence matrices

Radiomics in prostate cancer: an up-to-date review

: Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications

AI-enhanced diagnosis of challenging lesions in breast MRI: a methodology and application primer

Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist’s review. CAD systems have many common parts such as image pre-processing, tumor feature extraction and data classification that are mostly based on machine learning (ML) techniques. In this review paper, we describe the application of ML-based CAD systems in MRI of the breast covering the detection of diagnostically challenging lesions such as non-mass enhancing (NME) lesions, multiparametric MRI, neo-adjuvant chemotherapy (NAC) and radiomics all applied to NME. Since ML has been widely used in the medical imaging community, we provide an overview about the state-ofthe-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples illustrating: (i) CAD for the detection and diagnosis, (ii) CAD in multi-parametric imaging (iii) CAD in NAC and (iv) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on ANN in MRI of the breast