A learning-based CT prostate segmentation method via joint transductive feature selection and regression

In1 recent years, there has been a great interest in prostate segmentation, which is a important and challenging task for CT image guided radiotherapy. In this paper, a learning-based segmentation method via joint transductive feature selection and transductive regression is presented, which incorporates the physician’s simple manual specification (only taking a few seconds), to aid accurate segmentation, especially for the case with large irregular prostate motion. More specifically, for the current treatment image, experienced physician is first allowed to manually assign the labels for a small subset of prostate and non-prostate voxels, especially in the first and last slices of the prostate regions. Then, the proposed method follows the two step: in prostate-likelihood estimation step, two novel algorithms: tLasso and wLapRLS, will be sequentially employed for transductive feature selection and transductive regression, respectively, aiming to generate the prostate-likelihood map. In multi-atlases based label fusion step, the final segmentation result will be obtained according to the corresponding prostate-likelihood map and the previous images of the same patient. The proposed method has been substantially evaluated on a real prostate CT dataset including 24 patients with 330 CT images, and compared with several state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of higher Dice ratio, higher true positive fraction, and lower centroid distances. Also, the results demonstrate that simple manual specification can help improve the segmentation performance, which is clinically feasible in real practice

Distortion Robust Biometric Recognition

abstract: Information forensics and security have come a long way in just a few years thanks to the recent advances in biometric recognition. The main challenge remains a proper design of a biometric modality that can be resilient to unconstrained conditions, such as quality distortions. This work presents a solution to face and ear recognition under unconstrained visual variations, with a main focus on recognition in the presence of blur, occlusion and additive noise distortions. First, the dissertation addresses the problem of scene variations in the presence of blur, occlusion and additive noise distortions resulting from capture, processing and transmission. Despite their excellent performance, ’deep’ methods are susceptible to visual distortions, which significantly reduce their performance. Sparse representations, on the other hand, have shown huge potential capabilities in handling problems, such as occlusion and corruption. In this work, an augmented SRC (ASRC) framework is presented to improve the performance of the Spare Representation Classifier (SRC) in the presence of blur, additive noise and block occlusion, while preserving its robustness to scene dependent variations. Different feature types are considered in the performance evaluation including image raw pixels, HoG and deep learning VGG-Face. The proposed ASRC framework is shown to outperform the conventional SRC in terms of recognition accuracy, in addition to other existing sparse-based methods and blur invariant methods at medium to high levels of distortion, when particularly used with discriminative features. In order to assess the quality of features in improving both the sparsity of the representation and the classification accuracy, a feature sparse coding and classification index (FSCCI) is proposed and used for feature ranking and selection within both the SRC and ASRC frameworks. The second part of the dissertation presents a method for unconstrained ear recognition using deep learning features. The unconstrained ear recognition is performed using transfer learning with deep neural networks (DNNs) as a feature extractor followed by a shallow classifier. Data augmentation is used to improve the recognition performance by augmenting the training dataset with image transformations. The recognition performance of the feature extraction models is compared with an ensemble of fine-tuned networks. The results show that, in the case where long training time is not desirable or a large amount of data is not available, the features from pre-trained DNNs can be used with a shallow classifier to give a comparable recognition accuracy to the fine-tuned networks.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Segmentación del hipocampo en imágenes de resonancia magnética utilizando un modelo de forma activa

Actualmente, el uso de las imágenes médicas tiene impacto en el área clínica, gracias a que el desarrollo científico y tecnológico permite a los médicos hacer un análisis y diagnóstico para distintas patologías del cerebro y otras estructuras anatómicas. La segmentación del área del hipocampo es de interés en el área médica, debido a que se considera un biomarcador para el diagnóstico de patologías neurológicas y psiquiátricas, incluyendo enfermedad de Alzheimer (EA), epilepsia y esquizofrenia (Dill et al., 2015; Boccardi et al., 2015), así como para revelar las diferencias anatómicas (atrofia) de personas, debido al envejecimiento o la demencia (Kim et al., 2013), su anatomía puede ser analizada con neuroimágenes médicas, por ejemplo, las imágenes de resonancia magnética (IRM). Dicha segmentación de la estructura anatómica puede ser de forma manual, semiautomática o automática. En esta tesis se evalúa un método para la segmentación de la forma del hipocampo en imágenes de resonancia magnética, utilizando un modelo de forma activa (ASM, por sus siglas en inglés Active Shape Model), el cual es utilizado en dos etapas: entrenamiento y ajuste. En la etapa del entrenamiento de ASM se utiliza un conjunto de imágenes segmentadas manualmente que sirven para formar un modelo de distribución de puntos (MDP), donde cada forma es representada por un conjunto de puntos que describen el borde de una estructura. Por otra parte, la etapa de ajuste consiste en segmentar nuevas formas en el que se analizan los niveles de gris alrededor de cada punto de referencia de la forma. Además, se utiliza una métrica de distancia (distancia euclidiana) con la que se mide la distancia entre los puntos de la segmentación manual y la segmentación ajustada con ASM para obtener los errores de ajuste. El modelo de forma activa fue construido con 41 imágenes de resonancia magnética tomadas de la base de datos Alzheimer’s Disease Neuroimagen Initiative (ADNI), de la Universidad del Sur de California (University, 2020). Las imágenes del conjunto de entrenamiento fueron marcadas con 30 puntos de referencia, dado que el hipocampo es una estructura anatómica cuya dimensión es de 4 a 4.5 cm de longitud y de 1 a 1.5 cm de ancho (Duvernoy, 2013). Se presenta una experimentación para validar el nivel de ajuste del modelo de forma activa, previo a una consistente revisión literaria del estado del arte. La experimentación de ajuste se realiza utilizando la técnica leave one out. En dicha experimentación se obtiene un error de ajuste medio de 1.85 mm, el cual está por debajo del máximo error permisible (2 mm) en diagnósticos clínicos (Yue et al., 2006), lo cual indica que son resultados aceptables. Por otra parte, se obtuvo el coeficiente de similitud de Dice (DSC, por sus siglas en inglés Dice Similarity Coefficient) para cuantificar la precisión de la segmentación. El resultado del DSC medio es de 62 %, lo cual indica un resultado por debajo del valor aceptable que es de 80%.PNPC CONACY