Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images

Most thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in ultrasound images. Numerous textural feature extraction methods are used to characterize these patterns to reduce the misdiagnosis rate. Thyroid nodules can be classified using the corresponding textural features. In this paper, six support vector machines (SVMs) are adopted to select significant textural features and to classify the nodular lesions of a thyroid. Experiment results show that the proposed method can correctly and efficiently classify thyroid nodules. A comparison with existing methods shows that the feature-selection capability of the proposed method is similar to that of the sequential-floating-forward-selection (SFFS) method, while the execution time is about 3-37 times faster. In addition, the proposed criterion function achieves higher accuracy than those of the F-score, T-test, entropy, and Bhattacharyya distance methods.     

A symbolic system for computer-aided development of surface interpolants

The design and implementation of a symbolic input end computation package and its application to the development of several new surface interpolation schemes are described. Capabilities such as the composition of operators and symbolic differentiation have been incorporated into the system. This allows, in particular, the specification of Boolean sum projectors. The new schemes which have been implemented include an interpolant to randomly spaced data and a 'shape operator1 which has quadratic precision.     

A genetically optimized level set approach to segmentation of thyroid ultrasound images

This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC incorporates a level set approach named Variable Background Active Contour model (VBAC) that utilizes variable background regions, to reduce the effects of the intensity inhomogeneity in the thyroid ultrasound images. Moreover, a parameter tuning mechanism based on Genetic Algorithms (GA) has been considered to search for the optimal VBAC parameters automatically, without requiring technical skills. Experiments were conducted over a range of ultrasound images displaying thyroid nodules. The results show that the proposed GA-VBAC framework provides an efficient, effective and highly objective system for the delineation of thyroid nodules.     

Recognition of cylindrical-based features using edge boundary technique for integrated manufacturing

A new technique called edge boundary classification (EBC) for recognition of features from boundary representation (B-rep) solid model to support integrated manufacturing is presented. Features are interpreted from patterns formed by identifying the solid and void ‘sides’ of a boundary entity using the spatial addressability information of solid models. The application of the EBC technique for recognising simple and interacting cylindrical-based features is explained and discussed. The type of features experimented are through-hole, blind-hole, boss and internal undercut. The results obtained show that cylindrical-based features can easily be recognised using the EBC technique.     

Generation of functional tolerancing based on positioning features

The aim behind applying functional tolerancing to a mechanism is to widen the tolerances used in parts manufacturing according to the effective functional properties of the product. This step may be performed using CAD systems and geometrical specifications defined by ISO standards. The present paper will describe the complete process involved in functional tolerancing. The CLIC tolerancing method has been implemented within an Excel software environment. CAD models for parts have been imported via a STEP interface. According to this approach, the designer describes the assembly process; the CLIC system then determines the functional requirements corresponding to the joints between parts and generates all datum reference frames and tolerancing of set-up surfaces in compliance with ISO standards. CLIC also determines both the geometrical conditions of minimum distances in order to avoid interference between parts and the conditions for assembling small standard components. The designer next adds other functional requirements. For each such requirement, a tolerancing process creates location and orientation specifications for influential parts using datum reference frames derived during the previous stage. Excel formulae focusing on the sum of tolerances are generated using a three-dimensional statistical approach. Moreover, the tolerance database allows optimizing the tolerances and nominal dimensions of parts.     

A coarse-to-fine approach for segmenting melanocytic skin lesions in standard camera images

Melanoma is a type of malignant melanocytic skin lesion, and it is among the most life threatening existing cancers if not treated at an early stage. Computer-aided prescreening systems for melanocytic skin lesions is a recent trend to detect malignant melanocytic skin lesions in their early stages, and lesion segmentation is an important initial processing step. A good definition of the lesion area and its border is very important for discriminating between benign and malignant cases. In this paper, we propose to segment melanocytic skin lesions using a sequence of steps. We start by pre-segmenting the skin lesion, creating a new image representation (channel) where the lesion features are more evident. This new channel is thresholded, and the lesion border pre-detection is refined using an active-contours algorithm followed by morphological operations. Our experimental results based on a publicly available dataset suggest that our method potentially can be more accurate than comparable state-of-the-art methods proposed in literature.     

A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

Proper interpretation of the thyroid gland functional data is an important issue in the diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body’s metabolism. Thyroid hormone produced by the thyroid gland provides this. Production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism) defines the type of thyroid disease. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has shown an effective and intriguing performance on the problems it was applied. This study aims at diagnosing thyroid disease with a new hybrid machine learning method including this classification system. By hybridizing AIRS with a developed Fuzzy weighted pre-processing, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used thyroid disease dataset which is taken from UCI machine learning respiratory. We obtained a classification accuracy of 85%, which is the highest one reached so far. The classification accuracy was obtained via a 10-fold cross-validation.     

A method of boundary determination in digital images of urothelial cells

Digital images acquired by high resolution scanning of urothelial cells are segmented into nuclear and cytoplasmic regions. A method which is a hybrid of a gradient tracking method and a thresholding method is used, and criteria are presented to automatically detect failures of the algorithm. Tests on a sample of benign and malignant urothelial cells show a significant improvement in both accuracy and reliability as compared with a single segmentation method.     

Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains

Due to the complicated structure of breast and poor quality of ultrasound images, accurately and automatically locating regions of interest (ROIs) and segmenting tumors are challenging problems for breast ultrasound (BUS) computer-aided diagnosis systems. In this paper, we propose a fully automatic BUS image segmentation approach for performing accurate and robust ROI generation, and tumor segmentation. In the ROI generation step, the proposed adaptive reference point (RP) generation algorithm can produce the RPs automatically based on the breast anatomy; and the multipath search algorithm generates the seeds accurately and fast. In the tumor segmentation step, we propose a segmentation framework in which the cost function is defined in terms of tumor?s boundary and region information in both frequency and space domains. First, the frequency constraint is built based on the newly proposed edge detector which is invariant to contrast and brightness; and then the tumor pose, position and intensity distribution are modeled to constrain the segmentation in the spatial domain. The well-designed cost function is graph-representable and its global optimum can be found. The proposed fully automatic segmentation method is applied to a BUS database with 184 cases (93 benign and 91 malignant), and the performance is evaluated by the area and boundary error metrics. Compared with the newly published fully automatic method, the proposed method is more accurate and robust in segmenting BUS images.     

基于风险事件分类的信息系统评估模型研究

提出了一种基于风险事件分类的信息系统评估模型.模型将信息系统的单个风险事件按底层评估指标进行分类,得到底层指标风险值,然后按照评估指标的层次结构由下往上进行计算,并最终得到信息系统的综合风险值.最后,利用模糊综合评判法,在提出的模型上对一个信息系统进行评估,并得出评估结论.     

A Bayesian approach for object classification based on clusters of SIFT local features

Several methods have been presented in the literature that successfully used SIFT features for object identification, as they are reasonably invariant to translation, rotation, scale, illumination and partial occlusion. However, they have poor performance for classification tasks. In this work, SIFT features are used to solve object class recognition problems in images using a two-step process. In its first step, the proposed method performs clustering on the extracted features in order to characterize the appearance of the different classes. Then, in the classification step, it uses a three layer Bayesian network for object class recognition. Experiments show quantitatively that clusters of SIFT features are suitable to represent classes of objects. The main contributions of this paper are the introduction of a Bayesian network approach in the classification step to improve performance in an object class recognition task, and a detailed experimentation that shows robustness to changes in illumination, scale, rotation and partial occlusion.     

Extraction of karyocytes and their components from microscopic bone marrow images based on regional color features

Extracting karyocytes and their components from microscopic bone marrow images is prerequisite for computer-aided early diagnosis of hemopathy. Most existing methods assume all pixels belonging to a karyon region or a cytoplasm region have similar colors. Practically, the color of neither a karyon nor a cytoplasm in a microscopic image is homogeneous in the pixel level. Therefore, the regional color features of a region are emphasized in this paper. Based on this observation, we propose a novel method to karyocyte extraction that first identifies a karyon by 4-connected block growing from a karyon feature block, then identifies feature blocks of its cytoplasm based on the extracted karyon, and finally extracts all cytoplasm regions by growing the cytoplasm feature blocks. Combining the karyon region and the corresponding cytoplasm regions can attain a complete karyocyte. Experimental results show that the proposed method is effective and robust.     

基于时域特征的光纤安防系统信号识别算法

在分布式光纤周界安防系统中,为实时有效分辨出系统所受的入侵事件和干扰事件,提出了一种基于时域特征的振动信号模式识别算法.对原始振动信号进行降噪处理,然后提取降噪后信号的短时能量和短时过阈值率两类特征值,将提取的特征值作为特征向量输入到支持向量机(SVM)中进行训练和测试,建立分类模型,将分类模型应用于安防系统,实时识别出入侵和干扰事件.实验结果可知:模式识别算法的分类效率高,误差小,具有有效性和可行性.     

Model-free visualization of suspicious lesions in breast MRI based on supervised and unsupervised learning

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition techniques for computing enhanced visualizations of suspicious lesions in breast MRI data. These techniques represent an important component of future sophisticated computer-aided diagnosis (CAD) systems and support the visual exploration of spatial and temporal features of DCE-MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogeneity of cancerous tissue, these techniques reveal signals with malignant, benign and normal kinetics. They also provide a regional subclassification of pathological breast tissue, which is the basis for pseudo-color presentations of the image data. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.     

A new segmentation system for brain MR images based on fuzzy techniques

This work concerns a new method called fuzzy membership C-means (FMCMs) for segmentation of magnetic resonance images (MRI), and an efficient program implementation of it to the segmentation of MRI. Classical unsupervised clustering methods including the FCM by Bezdek, suffer many problems that can be partially treated with a proper rule to construct the initial membership matrix to clusters. This work develops a specific method to construct the initial membership matrix to clusters in order to improve the strength of the clusters. The new FMCM is tested on a set of benchmarks and then the application to the segmentation of MR images is presented and compared with the results obtained using FCM.     

A content-based system for human identification based on bitewing dental X-ray images

This paper presents a system for assisting in human identification using dental radiographs. The goal of the system is to archive antemortem (AM) dental images and enable content-based retrieval of AM images that have similar teeth shapes to a given postmortem (PM) dental image. During archiving, the system classifies the dental images to bitewing, periapical, and panoramic views. It then segments the teeth and the bones in the bitewing images, separates each tooth into the crown and the root, and stores the contours of the teeth in the database. During retrieval, the proposed system retrieves from the AM database the images with the most similar teeth to the PM image based on Hausdorff distance measure between the teeth contours. Experiments on a small database show that our method is effective for dental image classification and teeth segmentation, provides good results for separating each tooth into crown and root, and provides a good tool for human identification.     

化工过程计算机辅助计算的网络CAI系统的研究

化工专业有较多复杂公式和理论计算,MATLAB作为一种高级工程语言,可以简化其计算过程,但该过程仅采用多媒体课件,难以达到较好的授课效果。本文通过剖析化工过程计算机辅助计算的特点,给出了网络CAI系统开发的具体思路和方法。该系统由网络课件、练习指导、问题讨论等部分构成,是师生双方均不受时间和空间限制进行大容量信息沟通的桥梁．从而有效巩固课堂授课效果。     

A hybrid medical decision making system based on principles component analysis, k-NN based weighted pre-processing and adaptive neuro-fuzzy inference system

Proper interpretation of the thyroid gland functional data is an important issue in diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body's metabolism. Thyroid hormone produced by thyroid gland provides this. Production of too little thyroid hormone (hypo-thyroidism) or production of too much thyroid hormone (hyper-thyroidism) defines the types of thyroid disease. It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of thyroid disease, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on thyroid disease using principles component analysis (PCA), k-nearest neighbor (k-NN) based weighted pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The proposed system has three stages. In the first stage, dimension of thyroid disease dataset that has 5 features is reduced to 2 features using principles component analysis. In the second stage, a new weighting scheme based on k-nearest neighbor (k-NN) method was utilized as a pre-processing step before the main classifier. Then, in the third stage, we have used adaptive neuro-fuzzy inference system to diagnosis of thyroid disease. We took the thyroid disease dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.