Using VGG16 Algorithms for classification of lung cancer in CT scans Image

Lung cancer is the leading reason behind cancer-related deaths within the world. Early detection of lung nodules is vital for increasing the survival rate of cancer patients. Traditionally, physicians should manually identify the world suspected of getting carcinoma. When developing these detection systems, the arbitrariness of lung nodules' shape, size, and texture could be a challenge. Many studies showed the applied of computer vision algorithms to accurate diagnosis and classification of lung nodules. A deep learning algorithm called the VGG16 was developed during this paper to help medical professionals diagnose and classify carcinoma nodules. VGG16 can classify medical images of carcinoma in malignant, benign, and healthy patients. This paper showed that nodule detection using this single neural network had 92.08% sensitivity, 91% accuracy, and an AUC of 93%

Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening

The development of low-dose spiral computed tomography (CT) has rekindled hope that effective lung cancer screening might yet be found. Screening is justified when there is evidence that it will extend lives at reasonable cost and acceptable levels of risk. A screening test should detect all extant cancers while avoiding unnecessary workups. Thus optimal screening modalities have both high sensitivity and specificity. Due to the present state of technology, radiologists must opt to increase sensitivity and rely on follow-up diagnostic procedures to rule out the incurred false positives. There is evidence in published reports that computer-aided diagnosis technology may help radiologists alter the benefit-cost calculus of CT sensitivity and specificity in lung cancer screening protocols. This review will provide insight into the current discussion of the effectiveness of lung cancer screening and assesses the potential of state-of-the-art computer-aided design developments

Automatic lungs and trachea segmentation on computed tomography images from the thorax

Orientador: Alexandre Xavier FalcãoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Atualmente, doenças respiratórias afetam uma grande parcela da população mundial. A melhor maneira para detecção e análise desse tipo de doença é o diagnóstico por imagem, principalmente a tomografia computadorizada (CT). Sistemas de apoio ao diagnóstico foram desenvolvidos para auxiliar especialistas clínicos na análise de imagens de CT e na obtenção de diagnósticos precisos e rápidos. Em tais sistemas, a segmentação dos pulmões é um passo primordial a ser realizado, sendo fundamental para análises quantitativas. Ao longo dos últimos anos, muitos métodos de segmentação dos pulmões foram propostos. Entretanto, eles sofrem de pelo menos uma das seguintes limitações: alto tempo computacional, fracas condições para separação da traqueia e de cada pulmão, e um número limitado de amostras para validação. Abordando tais limitações, esta dissertação de mestrado propõe um método rápido e automático chamado Automatic Lung and Trachea Image Segmentation (ALTIS), para segmentação dos pulmões e da traqueia em imagens de CT do tórax. O método ALTIS se fundamenta na competição ótima de sementes para segmentar os pulmões e a traqueia em tempo proporcional ao tamanho da imagem. Ele consiste de uma rápida sequência de operações de processamento de imagem baseadas em características anatômicas que são robustas para a maioria das variações de forma e aparência dos pulmões. Isto é, a partir da premissa de que o sistema respiratório em uma imagem de CT representa o maior objeto cercado por tecido mais claro, os pulmões e a traqueia são extraídos criando o volume de interesse. Considerando que os pulmões direito e esquerdo são mais largos do que a traqueia e que a traqueia é um objeto longo e distante dos pulmões, sementes com rótulos diferentes dentro de cada um desses componentes são estimadas por meio de transformadas de distância. A competição ótima de sementes propaga esses rótulos para o resto do volume de interesse, realizando o delineamento dos objetos. O delineamento é feito nos volumes de ar da traqueia e dos pulmões até a pleura visceral. Assim, a segmentação da cavidade pleural está fora do escopo deste projeto. O método ALTIS foi extensivamente avaliado em um conjunto de aproximadamente 1.750 imagens de tomografia, unindo tanto bases de dados internas como públicas. Até onde sabemos, esse é o maior conjunto de imagens para validação dentre os trabalhos reportados na literatura. Além do método ALTIS, outros dois métodos baseados em modelos de forma, MALF e SOSM-S, foram quantitativamente avaliados em 250 imagens desse conjunto. Essa avaliação foi feita através da análise de sobreposição e distância até a borda das segmentações interativas consideradas corretas. Os experimentos realizados indicaram que o método ALTIS é estatisticamente superior e consideravelmente mais rápido que ambos os métodos comparados. As 1.500 imagens restantes foram utilizadas para verificação da robustez do método proposto. Nesta etapa, cada uma das imagens de segmentação geradas pelo ALTIS foram visualmente examinadas à procura de falhas. Foram observados erros de segmentação em uma pequena porcentagem delasAbstract: On the present day, respiratory diseases affect a great portion of people worldwide. The best way to detect and analyze this kind of disease is by diagnostic imaging, mainly Computed Tomography (CT). Computer-aided diagnosis systems have been developed to help specialists with the analysis of CT images to obtain an accurate and fast diagnosis. In such systems, the segmentation of the lungs is paramount for quantitative analysis. In the past years, many lung segmentation methods have been proposed. However,they suffer from at least one of these limitations: high computational time, weak conditions for separating the trachea and each lung with internal structures brighter than the air, and a limited number of samples for validation. Addressing those limitations, this master¿s thesis proposes a fast lung and trachea segmentation method, called Automatic Lung and Trachea Image Segmentation (ALTIS), on CT images from the thorax. The ALTIS method uses optimum seed competition to segment the lungs and the trachea in a time proportional to the domain of the image. It consists of a fast sequence of image processing operations which takes into consideration anatomical characteristics that are robust for most appearance variations of abnormal lungs. That is, from the premise that the respiratory system on a CT image represents the largest object surrounded by brighter tissue, the lungs and the trachea are extracted creating the volume of interest. Considering that the lungs are larger than the trachea and the trachea is a thin object distant from the lungs, seeds with different labels are estimated inside each component by means of distance transforms. The optimum seed competition propagates these labels to the rest of the volume of interest, delineating the objects. The delineation is performed on the volumes of air inside the trachea and both lungs, limited by the visceral pleura. Therefore, the segmentation of the pleural cavity is not within the scope of this project. The ALTIS method was extensively evaluated on a set of approximately 1.750 CT images, gathering both in-house and public datasets. To the best of our knowledge, this is the largest set of CT images used for validation ever reported in the literature. Besides ALTIS, two other shape model-based methods, MALF and SOSM-S, were quantitatively evaluated on 250 CT images from the original set. This evaluation was made through overlapping and border distance analysis over the ground-truth segmentations. The performed experiments indicated that ALTIS is statistically superior and considerably faster than both compared methods. The remaining 1.500 images were used to verify ALTIS¿s robustness. At this stage, each ALTIS¿s segmentation was visually analyzed on the search for failures. Segmentation errors were observed in a small percentage of themMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

A neural network-based vision system for automated separation of onion from clod using mechanical harvester

A neural network classifier for separating clods from onions during harvesting has been developed. The separator consists of a multi-layer feedforward network that maps textural features computed from gray-scale images of onions and clods into the correct object. Texture features were computed from co-occurrence matrices that specify the spatial relationship of pixel values in an image. The textural features selected for this application consist of homogeneity, contrast, variance, and energy. The network was trained using the back-propagation algorithm. Based on the textural features classification, the effect of changing the network configuration on separation effectiveness was investigated. Factors including network topology and combination of textural feature measures forming the inputs of the network were systematically analyzed. Thirty three different network configurations were evaluated. The best separation effectiveness was obtained with three-layer (3-2-1) network with input set consisting of energy, contrast, and homogeneity feature measures. The separation effectiveness for 3-2-1 network topology was 96 percent. An analysis of integration of the neural network-based vision system with a mechanical separator is presented

Study on Temporal Image Analysis from Thoracic CT Image

九州工業大学博士学位論文 学位記番号:工博甲第363号　学位授与年月日:平成26年3月25日第1章 序論|第2章 適応的細分化による三次元胸部CT画像の階層的レジストレーション手法|第3章 血管構造情報を用いた三次元胸部CT 画像における非剛体レジストレーション手法|第4章 濃度分布差に起因する経時的差分画像のアーチファクトの低減法|第5章 経時的濃度特徴量を用いた結節状陰影の検出|第6章 結論九州工業大学平成25年

Study on Temporal Image Analysis from Thoracic CT Image

九州工業大学博士学位論文 学位記番号:工博甲第363号　学位授与年月日:平成26年3月25日第1章 序論||第2章 適応的細分化による三次元胸部CT画像の階層的レジストレーション手法||第3章 血管構造情報を用いた三次元胸部CT 画像における非剛体レジストレーション手法||第4章 濃度分布差に起因する経時的差分画像のアーチファクトの低減法||第5章 経時的濃度特徴量を用いた結節状陰影の検出||第6章 結

On the development of a novel detector for simultaneous imaging and dosimetry in radiotherapy

Radiotherapy uses x-ray beams to deliver prescribed radiation doses that conform to target anatomy and minimise exposure of healthy tissue. Accuracy of dose delivery is essential, thus verification of dose distributions in vivo is desirable to monitor treatments and prevent errors from compromising patient outcomes. Electronic portal imaging devices (EPIDs) are commonly used x-ray imagers, however their non water-equivalent response complicates use for dosimetry. In this thesis, a Monte Carlo (MC) model of a standard EPID was developed and extended to novel water-equivalent configurations based on prototypes in which the high atomic number components were replaced with an array of plastic scintillator fibres. The model verified that full simulation of optical transport is not necessary to predict the standard EPID dose response, which can be accurately quantified from energy deposited in the phosphor screen. By incorporating computed tomography images into the model, its capacity to predict portal dose images of humanoid anatomy was also demonstrated. The prototype EPID’s water-equivalent dose response was characterised experimentally and with the MC model. Despite exhibiting lower spatial resolution and contrast-to-noise ratio relative to the standard EPID, its image quality was sufficient to discern gross anatomical structures of an anthropomorphic phantom. Opportunities to improve imaging performance while maintaining a water-equivalent dose response were identified using the model. Longer fibres increased efficiency and use of an extra-mural absorber maximised spatial resolution. Optical coupling between the scintillator fibres and the imaging panel may further improve performance. This thesis demonstrates the feasibility of developing a next-generation EPID for simultaneous imaging and dosimetry in radiotherapy. Such a detector could monitor treatment deliveries in vivo and thereby facilitate adaptations to treatment plans in order to improve patient outcomes

On the development of a novel detector for simultaneous imaging and dosimetry in radiotherapy

Radiotherapy uses x-ray beams to deliver prescribed radiation doses that conform to target anatomy and minimise exposure of healthy tissue. Accuracy of dose delivery is essential, thus verification of dose distributions in vivo is desirable to monitor treatments and prevent errors from compromising patient outcomes. Electronic portal imaging devices (EPIDs) are commonly used x-ray imagers, however their non water-equivalent response complicates use for dosimetry. In this thesis, a Monte Carlo (MC) model of a standard EPID was developed and extended to novel water-equivalent configurations based on prototypes in which the high atomic number components were replaced with an array of plastic scintillator fibres. The model verified that full simulation of optical transport is not necessary to predict the standard EPID dose response, which can be accurately quantified from energy deposited in the phosphor screen. By incorporating computed tomography images into the model, its capacity to predict portal dose images of humanoid anatomy was also demonstrated. The prototype EPID’s water-equivalent dose response was characterised experimentally and with the MC model. Despite exhibiting lower spatial resolution and contrast-to-noise ratio relative to the standard EPID, its image quality was sufficient to discern gross anatomical structures of an anthropomorphic phantom. Opportunities to improve imaging performance while maintaining a water-equivalent dose response were identified using the model. Longer fibres increased efficiency and use of an extra-mural absorber maximised spatial resolution. Optical coupling between the scintillator fibres and the imaging panel may further improve performance. This thesis demonstrates the feasibility of developing a next-generation EPID for simultaneous imaging and dosimetry in radiotherapy. Such a detector could monitor treatment deliveries in vivo and thereby facilitate adaptations to treatment plans in order to improve patient outcomes

Applications of aerospace technology in the public sector. An annual review of the Biomedical and public Sector Technology Application Team program for the period 1 June 1970 - 31 May 1971

Transfer of aerospace technology to nonaerospace problems -NASA projec