一种新颖的乳腺X线影像中钙化点检测方法

乳腺癌是妇女常见恶性肿瘤之一,早期诊断和早期治疗是降低乳腺癌患者死亡率的关键。微钙化是乳腺癌早期的一个重要标志,因此,快速准确地找出乳腺X光片中的钙化点成为成功诊断的第一步。现有多种方法能用于检测钙化点并各有优缺点,其中典型的高斯-拉普拉斯算子（LOG）是有效方法之一,尽管其能较精确地检出钙化点的位置但检测效率低。级联形态学滤波算子的LOG改进了LOG的效率,但仍无法满足大规模普查的高效率要求。通过提出一维和二维LOG相级联的方法来实现高效实时的钙化点的检测,并通过实验证实了所提检测方法的有效性。     

Detection of breast abnormalities in digital mammograms using the electromagnetism-like algorithm

Multimedia Tools and Applications - The detection of abnormalities in the breast at an early stage can be so helpful for breast cancer treatment. Currently, mammography is the cheapest and the most...     

Automated detection of breast tumors using the asymmetry approach

A method for automated detection of breast tumors in mammograms is presented. The method uses the asymmetry principle: Strong structural asymmetries between corresponding regions in the left and right breast are taken as evidence for the possible presence of a tumor in that region. Asymmetry detection is achieved in two steps. First, mammograms are aligned, compensating for possible differences in size and shape between the two breasts. Second, asymmetry between corresponding positions is determined using a combination of several asymmetry measures, each responding to different types of asymmetries. Results obtained with a set of mammograms indicate that this method can improve the sensitivity and reliability of systems for automated detection of breast tumors.     

Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm

In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection – CADe) and diagnosis (Computer-Aided Diagnosis – CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean ± standard deviation value of AOM for the proposed method was 79.2 ± 8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms

Neural Computing and Applications - Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided...     

An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms

The present study introduces an efficient algorithm for automatic segmentation and detection of mass present in the mammograms. The problem of over and under-segmentation of low-contrast mammographic images has been solved by applying preprocessing on original mammograms. Subtraction operation performed between enhanced and enhanced inverted mammogram significantly highlights the suspicious mass region in mammograms. The segmentation accuracy of suspicious region has been improved by combining wavelet transform and fast fuzzy c-means clustering algorithm. The accuracy of mass segmentation has been quantified by means of Jaccard coefficients. Better sensitivity, specificity, accuracy, and area under the curve (AUC) are observed with support vector machine using radial basis kernel function. The proposed algorithm is validated on Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets. Highest 91.76% sensitivity, 96.26% specificity, 95.46% accuracy, and 96.29% AUC on DDSM dataset and 94.63% sensitivity, 92.74% specificity, 92.02% accuracy, and 95.33% AUC on MIAS dataset are observed. Also, shape analysis of mass is performed by using moment invariant and Radon transform based features. The best results are obtained with Radon based features and achieved accuracies for round, oval, lobulated, and irregular shape of mass are 100%, 70%, 64%, and 96%, respectively.     

基于模糊数据挖掘与遗传算法的异常检测方法

建立合适的隶属度函数是入侵检测中应用模糊数据挖掘所面临的一个难点。针对这一问题,提出了在异常检测中运用遗传算法对隶属度函数的参数进行优化的方法。将隶属度函数的参数组合成有序的参数集并编码为遗传个体,在个体的遗传进化中嵌入模糊数据挖掘,可以搜索到最佳的参数集。采用这一参数集,能够在实时检测中最大限度地将系统正常状态与异常状态区分开来,提高异常检测的准确性。最后,对网络流量的异常检测实验验证了这一方法的可行性。     

Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm

A novel fuzzy logic and histogram based algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm is proposed for enhancing the local contrast of digital mammograms. A digital mammographic image uses a narrow range of gray levels. The contrast of a mammographic image distinguishes its diagnostic features such as masses and micro calcifications from one another with respect to the surrounding breast tissues. Thus, contrast enhancement and brightness preserving of digital mammograms is very important for early detection and further diagnosis of breast cancer. The limitation of existing contrast enhancement and brightness preserving techniques for enhancing digital mammograms is that they limit the amplification of contrast by clipping the histogram at a predefined clip-limit. This clip-limit is crisp and invariant to mammogram data. This causes all the pixels inside the window region of the mammogram to be equally affected. Hence these algorithms are not very suitable for real time diagnosis of breast cancer. In this paper, we propose a fuzzy logic and histogram based clipping algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm, which automates the selection of the clip-limit that is relevant to the mammogram and enhances the local contrast of digital mammograms. The fuzzy inference system designed to automate the selection of clip-limit requires a limited number of control parameters. The fuzzy rules are developed to make the clip limit flexible and variant to mammogram data without human intervention. Experiments are conducted using the 322 digital mammograms extracted from MIAS database. The performance of the proposed technique is compared with various histogram equalization methods based on image quality measurement tools such as Contrast Improvement Index (CII), Discrete Entropy (DE), Absolute Mean Brightness Coefficient (AMBC) and Peak Signal-to-Noise Ratio (PSNR). Experimental results show that the proposed FC-CLAHE algorithm produces better results than several state-of-art algorithms.     

A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms

Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research in this paper proposes a neural-genetic algorithm for feature selection to classify microcalcification patterns in digital mammograms. It aims to develop a step-wise algorithm to find the best feature set and a suitable neural architecture for microcalcification classification. The obtained results show that the proposed algorithm is able to find an appropriate feature subset, which also produces a high classification rate.     

A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence

Genetic algorithms (GAs) and neural networks (NNs) are both inspired by computation in biological systems and many attempts have been made to combine the two methodologies to boost the NNs performance. This paper deals with the evolutionary training of a feedforward NN for both breast cancer detection and recurrence. A multi‐layer perceptron (MLP) has been designed for this purpose, using a GA routine to set weights, and a Java implementation of this hybrid model has been made. Four databases concerning cancer detection and recurrence have been used, two databases containing numerical attributes only, one database containing ordinal (categorical) attributes solely and one database with mixed attributes. In comparison to some standard NNs, the performance of this approach using the same databases is shown to be superior. Moreover, this hybrid MLP/GA model is very flexible in terms of providing accurate classification, even with different types of attributes, which is usually found in medical studies.     

Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection

Neural networks have been employed in many medical applications including breast cancer classification. Innovation in diagnostic features of tumors may play a central role in development of new treatment methods for earliest stage of breast cancer detection. This study proposes a new hybrid for breast cancer detection by extending the application of a variation of particle swarm optimization called K-particle swarm optimization (KPSO). In this paper, the centers and variances of radial basis functional neural network are initialized by KPSO and then updated using back propagation. The weights are updated using recursive least square instead of back propagation. The results are compared with some recently developed techniques. It is found that the proposed technique provides more accurate result and better classification as compared to some other techniques.     

A high-performance approach on mechanism isomorphism identification based on an adaptive hybrid genetic algorithm for digital intelligent manufacturing

The graph theory is an important method to achieve conceptual design for mechanism. During the process of kinematic structures enumeration using graph theory, isomorphism identification of graphs is an NP complete problem. It is important to improve the isomorphism identification efficiency and reliability. To solve the problem, an adaptive hybrid genetic algorithm is presented by mixing the improved genetic algorithm and local search algorithm. The crossover rate and mutation rate can be designed as adaptive parameters. Hence, the crossover rate and mutation rate can sustain the variety of the population and adjust the evolution. In the meantime, the pseudo-crossover operator is introduced to improve the search efficiency. In the last, some examples are illustrated to show the high efficiency of the algorithm by comparing with the results in other literatures.     

New and efficient blind detection algorithm for digital image forgery using homomorphic image processing

Multimedia Tools and Applications - Digital image forgery detection is an important task in digital life as the image may be easily manipulated. This paper presents a novel blind tampering...     

A heuristic approach to robot path planning based on task requirements using a genetic algorithm

In order to enhance integration between CAD and robots, wer propose a scheme to plan kinematically feasible paths in the presence of obstacles based on task requirements. Thus, the feasibility of a planned path from a CAD system is assured before the path is sent for execution. The proposed scheme uses a heuristic approach to deal with a rather complex search space, involving high-dimensional C-space obstacles and task requirements specified in Cartesian space. When the robot is trapped by the local minimum in the potential field related to the heuristic, a genetic algorithm is then used to find a proper intermediate location that will guide it to escape out of the local minimum. For demonstration, simulations based on using a PUMA-typed robot manipulator to perform different tasks in the presence of obstacles were conducted. The proposed scheme can also be used for mobile robot planning. The paper falls into Category (5). Please address correspondence to the second author. This work was supported in part by the National Science Council, Taiwan, R.O.C., under grant NSC 82-0422-E-009-403.     

Integrated data structure and scheduling approach for manufacturing and transportation using hybrid genetic algorithm

The scheduling systems in industries are required to construct schedule considering many kind of elements. The Advanced Planning and Scheduling (APS) is an approach for combined problems. To realize APS system, it is important to integrate data structure and scheduling algorithm using these data. In this paper, we propose integrated data structure based on Bill of Manufacturing with information technology of XML family and new Multistage Operation-based Genetic Algorithm for scheduling subsystem. The results of numerical experiment validate effectiveness of the proposal methods. Received: June 2005/Accepted: December 2005     

Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection

This paper presents a novel system for automatic recognition of auditory brainstem responses (ABR) to detect hearing threshold. ABR is an important potential signal for determining objective audiograms. Its detection is usually performed by medical experts with often basic signal processing techniques. The proposed system comprises of two stages. In the first stage, for feature extraction, a set of raw amplitude values, a set of discrete cosine transform (DCT) coefficients and a set of discrete wavelet transform (DWT) approximation coefficients are calculated and extracted from signals separately as three different sets of feature vectors. These features are then selected by a modified adaptive method, which mainly supports to the input dimension reduction via selecting the most significant feature components. In the second stage, the feature vectors are classified by a support vector machine (SVM) classifier which is a powerful advanced technique for solving supervised binary classification problem due to its generalization ability. After that the proposed system is applied to real ABR data and it is resulted in a very good sensitivity, specificity and accuracy levels for DCT coefficients such as 99.2%, 94.0% and 96.2%, respectively. Consequently, the proposed system can be used for recognition of ABRs for hearing threshold detection.     

Design of efficient packing system using genetic algorithm based on hyper heuristic approach

In practice the maximum usage of container space arises in many applications which is one of the crucial economical requirements that have a wide impact on good transportation. A huge amount of monetary infrastructure is spent by companies on packing and transportation. This study recommends that there exists a scope for further optimization which if implemented can lead to huge saving. In this paper, we propose a new hyper heuristic approach which automates the design process for packing of two dimensional rectangular blocks. The paper contributes to the literature by introducing a new search technique where genetic algorithm is coupled with the hyper heuristic to get the optimal or sub optimal solution at an acceptable rate. The results obtained show the benefits of hyper-heuristic over traditional one when compared statistically on large benchmark dataset at the 5% level of significance. Improvements on the solution quality with high filling rate up to 99% are observed on benchmark instances.     

Application of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using Pittsburg approach

Genetic algorithm is well-known of its best heuristic search method. Fuzzy logic unveils the advantage of interpretability. Genetic fuzzy system exploits potential of optimization with ease of understanding that facilitates rules optimization. This paper presents the optimization of fourteen fuzzy rules for semi expert judgment automation of early activity based duration estimation in software project management. The goal of the optimization is to reduce linguistic terms complexity and improve estimation accuracy of the fuzzy rule set while at the same time maintaining a similar degree of interpretability. The optimized numbers of linguistic terms in fuzzy rules by 27.76% using simplistic binary encoding mechanism managed to improve accuracy by 14.29% and reduce optimization execution time by 6.95% without compromising on interpretability in addition to promote improvement of knowledge base in fuzzy rule based systems.