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Integrated feature analysis for prostate tissue characterization using TRUS images.

机译：使用TRUS图像进行前列腺组织表征的集成特征分析。

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The Prostate is a male gland that is located around the urethra. Prostate Cancer is the second most diagnosed malignancy in men over the age of fifty. Typically, prostate cancer is diagnosed from clinical data, medical images, and biopsy.; Computer Aided Diagnosis (CAD) was introduced to help in the diagnosis in order to assist in the biopsy operations. Usually, CAD is carried out utilizing either the clinical data, using data mining techniques, or using features extracted from either TransRectal UltraSound (TRUS) images or the Radio Frequency (RF) signals.; The challenge is that TRUS images' quality is usually poor compared to either Magnetic Resonance Imaging (MRI) or the Computed Tomography (CT). On the other hand, ultrasound imaging is more convenient because of its simple instrumentation and mobility capability compared to either CT or MRI. Moreover, TRUS is far less expensive and does not need certain settings compared to either MRI or CT. Accordingly; the main motivation of this research is to enhance the outcome of TRUS images by extracting as much information as possible from it. The main objective of this research is to implement a powerful noninvasive CAD tool that integrates all the possible information gathered from the TRUS images in order to mimic the expert radiologist opinion and even go beyond his visual system capabilities, a process that will in turn assist the biopsy operation. In this sense, looking deep in the TRUS images by getting some mathematical measures that characterize the image and are not visible by the radiologist is required to achieve the task of cancer recognition.; This thesis presents several comprehensive algorithms for integrated feature analysis systems for the purpose of prostate tissue classification. The proposed algorithm is composed of several stages, which are: First, the regions that are highly suspicious are selected using the proposed Gabor filter based ROI identification algorithm.; Second, the selected regions are further examined by constructing different novel as well as typical feature sets. The novel constructed feature sets are composed of statistical feature sets, spectral feature sets and model based feature sets.; Next, the constructed features were further analyzed by selecting the best feature subset that identifies the cancereus regions. This task is achieved by proposing different dimensionality reduction methods which can be categorized into: Classifier dependent feature selection (Mutual Information based feature selection), classifier independent feature selection, which is based mainly on tailoring the Artificial life optimization techniques to fit the feature selection problem and Feature Extraction, which transforms the data to a new lower dimension space without any degradation in the information and with no correlation among the transformed lower dimensional features.; Finally, the last proposed fragment in this thesis is the Spectral Clustering algorithm, which is applied to the TRUS images. Spectral Clustering is a novel fast algorithm that can be used in order to obtain a fast initial estimate of the cancer regions. Moreover, it can be used to support the decision obtained by the proposed cancer recognition algorithm. This decision support process is crucial at this stage as the gold standards used in obtaining the results shown in this thesis is mainly the radiologist's markings on the TRUS images. This gold standards is not considered as credible since the radiologist's best accuracy is approximately 65%.; In conclusion, this thesis introduces different novel complete algorithms for automatic cancerous regions detection in the prostate gland utilizing TRUS images. These proposed algorithms complement each other in which the results obtained using either of the proposed algorithms support each other by resulting in the same classification accuracy, sensitivity and specificity. This result proves the remarkable quality of the constructed features as we
机译：前列腺是位于尿道周围的雄性腺。前列腺癌是五十岁以上男性中第二被诊断出的恶性肿瘤。通常，从临床数据，医学图像和活检可诊断出前列腺癌。引入了计算机辅助诊断（CAD）来帮助诊断，以辅助活检操作。通常，使用临床数据，数据挖掘技术或使用从TransRectal UltraSound（TRUS）图像或射频（RF）信号中提取的特征来进行CAD。挑战在于，与磁共振成像（MRI）或计算机断层扫描（CT）相比，TRUS图像的质量通常较差。另一方面，与CT或MRI相比，超声成像的仪器和移动能力简单，因此更为方便。此外，与MRI或CT相比，TRUS的价格要便宜得多，并且不需要某些设置。因此；这项研究的主要动机是通过从中提取尽可能多的信息来增强TRUS图像的结果。这项研究的主要目的是实现一个功能强大的无创CAD工具，该工具整合了从TRUS图像中收集的所有可能的信息，以便模仿放射线专家的意见，甚至超越他的视觉系统能力，这一过程反过来将有助于活检手术。从这个意义上讲，通过获得一些表征图像的放射学专家无法看到的TRUS图像中的深层图像，是实现癌症识别的任务。本文提出了用于前列腺组织分类的综合特征分析系统的几种综合算法。所提出的算法包括几个阶段：首先，使用所提出的基于Gabor滤波器的ROI识别算法选择高度可疑的区域。其次，通过构建不同的小说和典型特征集来进一步检查所选区域。新构造的特征集由统计特征集，光谱特征集和基于模型的特征集组成。接下来，通过选择识别癌变区域的最佳特征子集进一步分析构建的特征。该任务是通过提出不同的降维方法来实现的，这些方法可以归类为：分类器相关特征选择（基于互信息的特征选择），分类器独立特征选择，这主要基于定制人工生命优化技术以适应特征选择问题特征提取，将数据转换到新的低维空间，而不会降低信息的质量，并且在转换后的低维特征之间没有相关性。最后，本文最后提出的片段是光谱聚类算法，该算法被应用于TRUS图像。频谱聚类是一种新颖的快速算法，可用于获得癌症区域的快速初始估计。而且，它可以用来支持通过提出的癌症识别算法获得的决策。此决策支持过程在此阶段至关重要，因为获得本文中显示的结果所使用的金标准主要是TRUS图像上的放射科医生标记。由于放射科医生的最佳准确性约为65％，因此不认为该黄金标准可信。总之，本文介绍了利用TRUS图像在前列腺中自动检测癌性区域的各种新颖的完整算法。这些提出的算法相互补充，其中使用任何一种提出的算法获得的结果通过导致相同的分类准确性，敏感性和特异性而相互支持。这个结果证明了所构造特征的卓越品质，因为我们

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