Edge enhancement nucleus and cytoplast contour detector of cervical smear images.

This paper presents an edge enhancement nucleus and cytoplast contour (EENCC) detector to enable cutting the nucleus and cytoplast from a cervical smear cell image. To clean up noises from an image, this paper proposes a trim-meaning filter that can effectively remove impulse and Gaussian noises but still preserves the sharpness of object boundaries. In addition, a bigroup enhancer is proposed to make a clear-cut separation of the pixels lying in-between two objects. A mean vector difference enhancer is presented to suppress the gradients of noises and also to brighten the gradients of object contours. What is more, a relative-distance-error measure is put forward to evaluate the segmentation error between the extracted and target object contours. The experimental results show that all the aforementioned techniques proposed have performed impressively. Other than for cervical smear images, these proposed techniques can also be utilized in object segmentation of other images.     

Automatic cervical cell segmentation and classification in Pap smears

Cervical cancer is one of the leading causes of cancer death in females worldwide. The disease can be cured if the patient is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is Papanicolaou test or Pap test. In this research, a method for automatic cervical cancer cell segmentation and classification is proposed. A single-cell image is segmented into nucleus, cytoplasm, and background, using the fuzzy C-means (FCM) clustering technique. Four cell classes in the ERUDIT and LCH datasets, i.e., normal, low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL), and squamous cell carcinoma (SCC), are considered. The 2-class problem can be achieved by grouping the last 3 classes as one abnormal class. Whereas, the Herlev dataset consists of 7 cell classes, i.e., superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. These 7 classes can also be grouped to form a 2-class problem. These 3 datasets were tested on 5 classifiers including Bayesian classifier, linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural networks (ANN), and support vector machine (SVM). For the ERUDIT dataset, ANN with 5 nucleus-based features yielded the accuracies of 96.20% and 97.83% on the 4-class and 2-class problems, respectively. For the Herlev dataset, ANN with 9 cell-based features yielded the accuracies of 93.78% and 99.27% for the 7-class and 2-class problems, respectively. For the LCH dataset, ANN with 9 cell-based features yielded the accuracies of 95.00% and 97.00% for the 4-class and 2-class problems, respectively. The segmentation and classification performances of the proposed method were compared with that of the hard C-means clustering and watershed technique. The results show that the proposed automatic approach yields very good performance and is better than its counterparts.     

Monotonic sequences for image enhancement and segmentation

Both image enhancement and image segmentation are important pre-processing steps for various image processing fields including autonomous navigation, remote sensing, computer vision, and biomedical image analysis. Both methods have their merits and their short comings. It then becomes obvious to ask the question: is it possible to develop a new better image enhancement method which has the key elements from both segmentation and image enhancement techniques? The choice of the threshold level is a key task in image segmentation. There are other challenges of image segmentation. For example, it is very difficult to perform the image segmentation in poor data such as shadows and noise. Recently, a homothetic curves Fibonacci-based cross sections thresholding has been developed for the de-noising purposes. Is it possible to develop a new image cross sections thresholding method, which can be used for both segmentation and image enhancement purposes? This paper a) describes a unified approach for signal thresholding, b) extends cross sections concept by generating and using a new class of monotonic, piecewise linear, sequences (slowly or faster growing than Fibonacci numbers) of numbers; c) uses the extended sections concept to the image enhancement and segmentation applications. Extensive experimental evaluation demonstrates that the newly proposed monotonic sequences have great potential in image processing applications, including image segmentation and image enhancement applications. Moreover, study has shown that the generalized cross techniques are invariant under morphological transformations such as erosion, dilation, and median, able to be described analytically, can be implemented by using the look up table methods.     

A novel context sensitive multilevel thresholding for image segmentation

Most of the traditional histogram-based thresholding techniques are effective for bi-level thresholding and unable to consider spatial contextual information of the image for selecting optimal threshold. In this article a novel thresholding technique is presented by proposing an energy function to generate the energy curve of an image by taking into an account the spatial contextual information of the image. The behavior of this energy curve is very much similar to the histogram of the image. To incorporate spatial contextual information of the image for threshold selection process, this energy curve is used as an input of our technique instead of histogram. Moreover, to mitigate multilevel thresholding problem the properties of genetic algorithm are exploited. The proposed algorithm is evaluated on the number of different types of images using a validity measure. The results of the proposed technique are compared with those obtained by using histogram of the image and also with an existing genetic algorithm based context sensitive technique. The comparisons confirmed the effectiveness of the proposed technique.     

A novel active contour model for image segmentation using distance regularization term

In this paper, a novel active contour model (R-DRLSE model) based on level set method is proposed for image segmentation. The R-DRLSE model is a variational level set approach that utilizes the region information to find image contours by minimizing the presented energy functional. To avoid the time-consuming re-initialization step, the distance regularization term is used to penalize the deviation of the level set function from a signed distance function. The numerical implementation scheme of the model can significantly reduce the iteration number and computation time. The results of experiments performed on some synthetic and real images show that the R-DRLSE model is effective and efficient. In particular, our method has been applied to MR kidney image segmentation with desirable results.     

基于阈值法与区域生长法综合集成的图像分割法

本文首先对一类因照明不均匀使得图像质量受到一定影响的这样一类比较复杂的图像特点及其分割难点做分析,然后提出一种新的基于阈值法与区域生长法综合集成的图像分割方法。实验结果表明,该分割算法不仅适用于较简单的图像分割问题,而且适用于分割因照明不均匀使得图像质量受到一定影响的这样一类较复杂的图像的分割,且分割时间可进一步缩短。     

活动轮廓的演化控制及其在铁谱图像分割中的应用

为了提高活动轮廓(active contour,AC)对边缘特征局部极小值的搜索效率,从而提高其对铁谱图像的分割速度,提出了一种基于活动轮廓评价和演化行为控制的图像分割方法.首先,设计了一种基于矢量图的边缘指示函数(edge indicator,EI)的计算方法,相应的计算结果为活动轮廓模型建立了一个边缘指向更加明确的边缘指示场(edge indicator field,EIF).其次,设计了曲线EI值的无迹卡尔曼滤波模型,并基于此提出了活动轮廓边缘特征的跟踪和评价方法.最后,根据以上评价结果调整曲线模型的参数以控制其演化行为.这种参数调节机制保证了曲线模型参数在不同的区域具有不同的参数设置.试验结果表明,该算法显著地提高了控制演化过程的灵活性以及活动轮廓的收敛速度,并且它能够实现对各种形状磨粒的准确分割,不仅避免了弱边界区域的泄漏现象,而且能够有效滤除背景中的各种噪声干扰和非磨粒目标.     

Novel fuzzy active contour model with kernel metric for image segmentation

In this paper, a novel region-based fuzzy active contour model with kernel metric is proposed for a robust and stable image segmentation. This model can detect the boundaries precisely and work well with images in the presence of noise, outliers and low contrast. It segments an image into two regions – the object and the background by the minimization of a predefined energy function. Due to the kernel metric incorporated in the energy and the fuzziness of the energy, the active contour evolves very stably without the reinitialization for the level set function during the evolution. Here the fuzziness provides the model with a strong ability to reject local minima and the kernel metric is employed to construct a nonlinear version of energy function based on a level set framework. This new fuzzy and nonlinear version of energy function makes the updating of region centers more robust against the noise and outliers in an image. Theoretical analysis and experimental results show that the proposed model achieves a much better balance between accuracy and efficiency compared with other active contour models.     

The optimal All-Partial-Sums algorithm in commutative semigroups and its applications for image thresholding segmentation

The design and analysis of multidimensional All-Partial-Sums (APS) algorithms are considered. We employ the sequence length as the performance measurement criterion for APS algorithms and corresponding thresholding methods, which is more sophisticated than asymptotic time complexity under the straight-line program computation model. With this criterion, we propose the piling algorithm to minimize the sequence length, then we show this algorithm is an optimal APS algorithm in commutative semigroups in the worst case. The experimental results also show the algorithmic efficiency of the piling algorithm. Furthermore, the theoretical works of APS algorithm will help to construct the higher dimensional thresholding methods.     

Probability density difference-based active contour for ultrasound image segmentation

Because of its low signal/noise ratio, low contrast and blurry boundaries, ultrasound (US) image segmentation is a difficult task. In this paper, a novel level set-based active contour model is proposed for breast ultrasound (BUS) image segmentation. At first, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. The actual probability densities are calculated directly. For calculating the estimated probability densities, the probability density estimation method and background knowledge are utilized. The energy function is formulated with level set approach, and a partial differential equation is derived for finding the minimum of the energy function. For performing numerical computation, the derived partial differential equation is approximated by the central difference and non-re-initialization approach. The proposed method was operated on both the synthetic images and clinical BUS images for studying its characteristics and evaluating its performance. The experimental results demonstrate that the proposed method can model the BUS images well, be robust to noise, and segment the BUS images accurately and reliably.     

A new active contour remote sensing river image segmentation algorithm inspired from the cross entropy

The CV (Chan–Vese) model is a piecewise constant approximation of the Mumford and Shah model. It assumes that the original image can be segmented into two regions such that each region can be represented as constant grayscale value. In fact, the objective functional of the CV model actually finds a segmentation of the image such that the within-class variance is minimized. This is equivalent to the Otsu image thresholding algorithm which also aims to minimize the within-class variance. Similarly to the Otsu image thresholding algorithm, cross entropy is another widely used image thresholding algorithm and it finds a segmentation such that the cross entropy of the segmented image and the original image is minimized. Inspired from the cross entropy, a new active contour image segmentation algorithm is proposed. The region term in the new objective functional is the integral of the logarithm of the ratio between the grayscale of the original image and the mean value computed from the segmented image weighted by the grayscale of the original image. The new objective functional can be solved by the level set evolution method. A distance regularized term is added to the level set evolution equation so the level set need not be reinitialized periodically. A fast global minimization algorithm of the objective functional is also proposed which incorporates the edge term originated from the geodesic active contour model. Experimental results show that, the algorithm proposed can segment images more accurately than the CV model and the implementation speed of the fast global minimization algorithm is fast.     

基于二维阈值化与FCM相混合的图象快速分割方法

提出了一种将快速二维阈值化与模糊聚类相混合的图象分割方法，以进一步减少快速二维阈值分割中的噪声与错误分割。实验结果表明，利用这种方法分割信噪比较低的图象，能够在很短的时间内得到较为令人满意的分割结果。此外，本文还讨论了这一方法中隶属度函数的选取对分割结果的影响     

An image recognition method by rough classification for a scene image

We have recognized the regions of scene images for image recognition. First, the proposed segmentation method classifies images into several segments without using the Euclidian distance. We need several features to recognize regions. However, they are different for chromatic and achromatic colors. The regions are divided into three categories (black, achromatic, and chromatic). In this article, we focus on the achromatic category. The averages of the intensity and the fractal dimension features of the regions in the achromatic category are calculated. We recognize the achromatic region by using a neural network with suitable features. In order to show the effectiveness of the proposed method, we have recognized the regions. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

A robust active contour model driven by fuzzy c-means energy for fast image segmentation

In this paper, we propose a robust region-based active contour model driven by fuzzy c-means energy that draws upon the clustering intensity information for fast image segmentation. The main idea of fuzzy c-means energy is to quickly compute the two types of cluster center functions for all points in image domain by fuzzy c-means algorithm locally with a proper preprocessing procedure before the curve starts to evolve. The time-consuming local fitting functions in traditional models are substituted with these two functions. Furthermore, a sign function and a Gaussian filtering function are utilized to replace the penalty term and the length term in most models, respectively. Experiments on several synthetic and real images have proved that the proposed model can segment images with intensity inhomogeneity efficiently and precisely. Moreover, the proposed model has a good robustness on initial contour, parameters and different kinds of noise.     

An architecture for a CBR image segmentation system

Image segmentation is a crucial step in extracting information from a digital image. It is not easy to set up the segmentation parameter so that it gives the best fit over the entire set of images that need to be segmented. This paper proposes a novel method for image segmentation based on CBR. It describe the whole architecture, as well as the methods used for the various components of the systems, and shows how the technique performs on medical images.     

Multiphase image segmentation using a phase-field model

In this paper, we propose a new, fast, and stable hybrid numerical method for multiphase image segmentation using a phase-field model. The proposed model is based on the Allen-Cahn equation with a multiple well potential and a data-fitting term. The model is computationally superior to the previous multiphase image segmentation via Modica-Mortola phase transition and a fitting term. We split its numerical solution algorithm into linear and a nonlinear equations. The linear equation is discretized using an implicit scheme and the resulting discrete system of equations is solved by a fast numerical method such as a multigrid method. The nonlinear equation is solved analytically due to the availability of a closed-form solution. We also propose an initialization algorithm based on the target objects for the fast image segmentation. Finally, various numerical experiments on real and synthetic images with noises are presented to demonstrate the efficiency and robustness of the proposed model and the numerical method.     

矩不变调整的二维Shannon嫡图像分割及其快速实现

为了克服二维Shannon熵阈值法的缺陷,提出了一种使用矩不变法来调整二维直方图斜分Shannon熵的阈值分割方法。首先将二维直方图斜分原理运用到两种Shannon熵阈值法中,然后利用矩不变法从两种熵阈值法获取的阈值中选择最佳阈值,并提出二维直方图斜分Shannon熵阈值法的一般递推算法,最后将二维直方图分布特性与这种算法有机结合得到新型快速的递推算法。实验结果表明,提出的方法不仅分割效果优于当前的二维直方图斜分的最大熵阈值法,而且运行速度更快,约快4倍。     

Robust level set image segmentation via a local correntropy-based K-means clustering

It is still a challenging task to segment real-world images, since they are often distorted by unknown noise and intensity inhomogeneity. To address these problems, we propose a novel segmentation algorithm via a local correntropy-based K-means (LCK) clustering. Due to the correntropy criterion, the clustering algorithm can decrease the weights of the samples that are away from their clusters. As a result, LCK based clustering algorithm can be robust to the outliers. The proposed LCK clustering algorithm is incorporated into the region-based level set segmentation framework. The iteratively re-weighted algorithm is used to solve the LCK based level set segmentation method. Extensive experiments on synthetic and real images are provided to evaluate our method, showing significant improvements on both noise sensitivity and segmentation accuracy, as compared with the state-of-the-art approaches.     

改进主动轮廓线模型在图像分割中的应用

主动轮廓线模型(Active Contour Model,ACM),也称作蛇(Snake)模型,是一种常用的图像分割算法。在基于主动轮廓线的图像分割中,深度凹陷边界的逼近和弱边界区域的分割一直是一个难点。引入了一种局部纹理模型(Local Profile Model)匹配算法,通过匹配沿控制点法线方向像素和局部纹理模型可以确定弱边界区域的真实边界,并结合一种新的计算控制点曲率外力的算法,使得主动轮廓线模型能够逼近图像的深度凹陷区域的同时提高算法的收敛速度。实验结果表明,该方法是有效的。