The Effects of Perinodular Features on Solid Lung Nodule Classification.

Lung cancer is the most lethal malignant neoplasm worldwide, with an annual estimated rate of 1.8 million deaths. Computed tomography has been widely used to diagnose and detect lung cancer, but its diagnosis remains an intricate and challenging work, even for experienced radiologists. Computer-aided diagnosis tools and radiomics tools have provided support to the radiologist's decision, acting as a second opinion. The main focus of these tools has been to analyze the intranodular zone; nevertheless, recent works indicate that the interaction between the nodule and its surroundings (perinodular zone) could be relevant to the diagnosis process. However, only a few works have investigated the importance of specific attributes of the perinodular zone and have shown how important they are in the classification of lung nodules. In this context, the purpose of this work is to evaluate the impact of using the perinodular zone on the characterization of lung lesions. Motivated by reproducible research, we used a large public dataset of solid lung nodule images and extracted fine-tuned radiomic attributes from the perinodular and intranodular zones. Our best-evaluated model obtained an average AUC of 0.916, an accuracy of 84.26%, a sensitivity of 84.45%, and specificity of 83.84%. The combination of attributes from the perinodular and intranodular zones in the image characterization resulted in an improvement in all the metrics analyzed when compared to intranodular-only characterization. Therefore, our results highlighted the importance of using the perinodular zone in the solid pulmonary nodules classification process.

Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture.

Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PURPOSE: Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. METHODS: A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. RESULTS: Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. CONCLUSION: Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research.

Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.

Automatic weighing attribute to retrieve similar lung cancer nodules.

BACKGROUND: Cancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung cancer is the primary cause of cancer-related deaths in the world, and it diagnosis is a complex task for specialists and it presents some big challenges as medical image interpretation process, pulmonary nodule detection and classification. In order to aid specialists in the early diagnosis of lung cancer, computer assistance must be integrated in the imaging interpretation and pulmonary nodule classification processes. Methods of Content-Based Image Retrieval (CBIR) have been described as one promising technique to computer-aided diagnosis and is expected to aid radiologists on image interpretation with a second opinion. However, CBIR presents some limitations: image feature extraction process and appropriate similarity measure. The efficiency of CBIR systems depends on calculating image features that may be relevant to the case similarity analysis. When specialists classify a nodule, they are supported by information from exams, images, etc. But each information has more or less weight over decision making about nodule malignancy. Thus, finding a way to measure the weight allows improvement of image retrieval process through the assignment of higher weights to that attributes that best characterize the nodules. METHODS: In this context, the aim of this work is to present a method to automatically calculate attribute weights based on local learning to reflect the interpretation on image retrieval process. The process consists of two stages that are performed sequentially and cyclically: Evaluation Stage and Training Stage. At each iteration the weights are adjusted according to retrieved nodules. After some iterations, it is possible reach a set of attribute weights that optimize the recovery of similar nodes. RESULTS: The results achieved by updated weights were promising because was possible increase precision by 10% to 6% on average to retrieve of benign and malignant nodules, respectively, with recall of 25% compared with tests without weights associated to attributes in similarity metric. The best result, we reaching values over 100% of precision average until thirtieth lung cancer nodule retrieved. CONCLUSIONS: Based on the results, WED applied to the three vectors used attributes (3D TA, 3D MSA and InV), with weights adjusted by the process, always achieved better results than those found with ED. With the weights, the Precision was increased on average by 17.3% compared with using ED.

Grades computacionais na recuperação de imagens médicas baseada em conteúdo / Grid computing in the optimization of content-based medical images retrieval

OBJETIVO: Utilizar o poder de processamento da tecnologia de grades computacionais para viabilizar a utilização do algoritmo de medida de similaridade na recuperação de imagens baseada em conteúdo. MATERIAIS E MÉTODOS: A técnica de recuperação de imagens baseada em conteúdo é composta de duas etapas seqüenciais: análise de textura e algoritmo de medida de similaridade. Estas são aplicadas em imagens de joelho e cabeça, nas quais se avaliaram a eficiência em recuperar imagens do mesmo plano e a seqüência de aquisição em um banco de 2.400 imagens médicas para testar a capacidade de recuperação de imagens baseada em conteúdo. A análise de textura foi utilizada inicialmente para pré-selecionar as 1.000 imagens mais semelhantes a uma imagem de referência escolhida por um clínico. Essas 1.000 imagens foram processadas utilizando-se o algoritmo de medida de similaridade na grade computacional. RESULTADOS: A precisão encontrada na classificação por análise de textura foi de 0,54 para imagens sagitais de joelho e de 0,40 para imagens axiais de cabeça. A análise de textura foi útil como filtragem, pré-selecionando imagens a serem avaliadas pelo algoritmo de medida de similaridade. A recuperação de imagens baseada em conteúdo utilizando o algoritmo de medida de similaridade aplicado nas imagens pré-selecionadas por análise de textura resultou em precisão de 0,95 para as imagens sagitais de joelho e de 0,92 para as imagens axiais de cabeça. O alto custo computacional do algoritmo de medida de similaridade foi amortizado pela grade computacional. CONCLUSÃO: A utilização da abordagem mista das técnicas de análise de textura e algoritmo de medida de similaridade no processo de recuperação de imagens baseada em conteúdo resultou em eficiência acima de 90 por cento. A grade computacional é indispensável para utilização do algoritmo de medida de similaridade na recuperação de imagens baseada em conteúdo, que de outra forma seria limitado a supercomputadores.

OBJECTIVE: To utilize the grid computing technology to enable the utilization of a similarity measurement algorithm for content-based medical image retrieval. MATERIALS AND METHODS: The content-based images retrieval technique is comprised of two sequential steps: texture analysis and similarity measurement algorithm. These steps have been adopted for head and knee images for evaluation of accuracy in the retrieval of images of a single plane and acquisition sequence in a databank with 2,400 medical images. Initially, texture analysis was utilized as a preselection resource to obtain a set of the 1,000 most similar images as compared with a reference image selected by a clinician. Then, these 1,000 images were processed utilizing a similarity measurement algorithm on a computational grid. RESULTS: The texture analysis has demonstrated low accuracy for sagittal knee images (0.54) and axial head images (0.40). Nevertheless, this technique has shown effectiveness as a filter, pre-selecting images to be evaluated by the similarity measurement algorithm. Content-based images retrieval with similarity measurement algorithm applied on these pre-selected images has demonstrated satisfactory accuracy - 0.95 for sagittal knee images, and 0.92 for axial head images. The high computational cost of the similarity measurement algorithm was balanced by the utilization of grid computing. CONCLUSION: The approach combining texture analysis and similarity measurement algorithm for content-based images retrieval resulted in an accuracy of > 90 percent. Grid computing has shown to be essential for the utilization of similarity measurement algorithm in the content-based images retrieval that otherwise would be limited to supercomputers.

Software livre na investigação científica: NeuroCAD - uma ferramenta "open-source" para auxílio ao diagnóstico em neurologia / Free-software in scientific research: NeuroCAD - an open-source neurology computer-aides toolkit

Este artigo apresenta a implementação de uma ferramenta computacional "open-source" para o auxílio ao diagnóstico em neurologia - NeuroCAD. Esta ferramenta é resultado de uma parceria entre o Centro de Ciência das Imagens e Física Médica da Faculdade de Medicina de Ribeirão Preto e o Centro de Cirurgia de Epilepsia do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto, da Universidade de São Paulo. O NeuroCAD foi desenvolvido em plataforma Linux sob uma filosofia "open-source", buscando suprir as necessidades apresentadas pelos profissionais do CIREP quanto ao processamento de imagens para auxílio ao diagnóstico clínico da Epilepsia do Lobo Temporal. A ferramenta é constituída por três módulos: a) um módulo de corregistro de imagens (anatômicas e funcionais), no qual o especialista posiciona marcadores e seus respectivos pares, possibilitando operações geométricas de escala, translação e rotação; b) um módulo de análise volumétrica com segmentação manual (o sistema armazena todos os objetos marcados e aplica o método de Cavalieri para cálculo de volume); c) um módulo de visualização, no qual as estruturas marcadas são reconstruídas tridimensionalmente