Morphology-based approaches for detecting stream channels from ALSM data

To map the Earth's surface at remarkable resolution, Airborne Laser Swath Mapping (ALSM) instrument technology and subsequent algorithms have been used over the last several years. Since forested watersheds have commonly been problematic to study with remote sensing techniques, the ability of ALSM technology to densely sample ground elevations beneath forest canopies is especially considerable. Stream network detection from digital elevation models (DEMs) is a key role in modelling spatially distributed hydrological processes. To detect stream channels, we have developed two approaches. The first approach is based on an encoding of mathematical morphological operators. In the second approach, a composition of geodesic top-hat and bot-hat operations of different sizes is used in order to build a morphological profile (P _M) that records the image structural information. The two proposed methods perform well in terms of detection results and classification accuracies. The second approach is more general than the first, but it also requires training and more computation.     

Strong fuzzy c-means in medical image data analysis

This paper presents a robust fuzzy c-means (FCM) for an automatic effective segmentation of breast and brain magnetic resonance images (MRI). This paper obtains novel objective functions for proposed robust fuzzy c-means by replacing original Euclidean distance with properties of kernel function on feature space and using Tsallis entropy. By minimizing the proposed effective objective functions, this paper gets membership partition matrices and equations for successive prototypes. In order to reduce the computational complexity and running time, center initialization algorithm is introduced for initializing the initial cluster center. The initial experimental works have done on synthetic image and benchmark dataset to investigate the effectiveness of proposed, and then the proposed method has been implemented to differentiate the different region of real breast and brain magnetic resonance images. In order to identify the validity of proposed fuzzy c-means methods, segmentation accuracy is computed by using silhouette method. The experimental results show that the proposed method is more capable in segmentation of medical images than existed methods.     

A multidimensional segmentation evaluation for medical image data

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.     

Reversible data hiding in encrypted image with separable data extraction from image decryption

We propose a method of reversible data hiding in encrypted image. Proposed method achieves reserving room before encryption, and separates data extraction from image decryption. Our method is an improvement of Ma’s method [IEEE Trans Inf Forensic Secur 8(3):554–558, 2013]. Our improvements mainly focus on two aspects. (1) We improved the interpolation error estimate method via using Bicubic interpolation instead of pixel estimation that calculates the weighted sum of four surrounding pixels. Thus more sharp interpolation error histogram is obtained to increase the hidden information capacity. (2) We use partitioned local histogram shift instead of traditional histogram shift to reduce the amount of shifted pixels. This directly results in a higher quality of stego image in the same embedding capacity. The experimental results indicate that the improved method offers better performance. It is superior to Ma’s work in both embedding rate and the PSNR values of stego images.     

Morphology-based text line extraction

This paper presents a morphology-based text line extraction algorithm for extracting text regions from cluttered images. First of all, the method defines a novel set of morphological operations for extracting important contrast regions as possible text line candidates. The contrast feature is robust to lighting changes and invariant against different image transformations like image scaling, translation, and skewing. In order to detect skewed text lines, a moment-based method is then used for estimating their orientations. According to the orientation, an x-projection technique can be applied to extract various text geometries from the text-analogue segments for text verification. However, due to noise, a text line region is often fragmented to different pieces of segments. Therefore, after the projection, a novel recovery algorithm is then proposed for recovering a complete text line from its pieces of segments. After that, a verification scheme is then proposed for verifying all extracted potential text lines according to their text geometries. Experimental results show that the proposed method improves the state-of-the-art work in terms of effectiveness and robustness for text line detection.     

Extracting area objects from raster image data

An algorithm for extracting the spatial objects contained in raster image data is presented. Input to the algorithm is a Siemens computer-aided design (SICAD)-Hygris raster file representing a classification of the plane. Output of the algorithm is a SICAD geographic information system (GIS) external file containing the topologically complete definitions of all area objects and their constituents, such as arcs and nodes, as implied by the classification. The algorithm is based on a one-pass plane sweep that processes the input raster data in a strictly sequential order. Processing takes place only if certain color changes, also called events, occur. Each event triggers a well-defined sequence of simple actions     

基于形态学和结构图表的指纹识别

指纹识别技术是目前最方便、可靠、非侵害和价格便宜的生物识别技术解决方案,对于广大市场的应用有着很大的潜力。为克服指纹识别中常见的拒识或误识问题,文章提出了一种基于结构图表的匹配算法以提高识别率,并将形态学技术成功的运用于指纹图像预处理中,以减少图像噪音干扰。整个算法具有高效识别和强抗噪能力,使指纹识别系统更具有鲁棒性。     

A MRI image segmentation method based on medical semaphore calculating in medical multimedia big data environment

Multimedia Tools and Applications - In this paper, we calculate global signal distribution of medical images, including intensity distribution and direction of the signal. We can get a global...     

Optimizing a medical image registration algorithm based on profiling data for real-time performance

Image registration is a commonly task in medical image analysis. Therefore, a significant number of algorithms have been developed to perform rigid and non-rigid image registration. Particularly, the free-form deformation algorithm is frequently used to carry out non-rigid registration task; however, it is a computationally very intensive algorithm. In this work, we describe an approach based on profiling data to identify potential parts of this algorithm for which parallel implementations can be developed. The proposed approach assesses the efficient of the algorithm by applying performance analysis techniques commonly available in traditional computer operating systems. Hence, this article provides guidelines to support researchers working on medical image processing and analysis to achieve real-time non-rigid image registration applications using common computing systems. According to our experimental findings, significant speedups can be accomplished by parallelizing sequential snippets, i.e., code regions that are executed more than once. For the selected costly functions previously identified in the studied free-form deformation algorithm, the developed parallelization decreased the runtime by up to seven times relatively to the related single thread based implementation. The implementations were developed based on the Open Multi-Processing application programming interface. In conclusion, this study confirms that based on the call graph visualization and detected performance bottlenecks, one can easily find and evaluate snippets which are potential optimization targets in addition to throughput in memory accesses.

     

Deniably authenticated searchable encryption scheme based on Blockchain for medical image data sharing

Multimedia Tools and Applications - In the cloud applications of medical data based on blockchain, doctors and managers usually want to obtain image data shared by other healthcare institutions. To...     

Tapetool: a software tool for importing image data from image acquisition computers to image processing computers

Currently, the image distribution gap between image acquisition computers and image processing computers is bridged through magnetic tapes. The tape formats used by the manufacturers of the image acquisition computers are idiosyncratic and fairly complex. A general purpose window based software tool is herein described, which frees the clinical and research sectors of the responsibility of understanding and decoding these complex formats in order to import image databases from acquisition computers to image processing computers. This software tool provides a cornerstone for developing image processing software for diagnostic, therapeutic and surgical planning purposes.     

基于内容的图像检索技术与医学图像检索

在分析基于内容的图像检索技术特点的基础上,提出了4种基于内容的图像检索方法,并对每种方法的实现特别是特征抽取进行了一定的研究。根据医学图像的使用特点,对基于内容的医学图像检索技术进行了初步的研究;对医学图像特征的抽取,应将重点放在形状特征和纹理特征的抽取上;同时,对医学图像进行检索,还可以使用颜色空间分布特征,来进一步进行相似匹配。     

Morphology-based black and white filters for topology optimization

To ensure manufacturability and mesh independence in density-based topology optimization schemes, it is imperative to use restriction methods. This paper introduces a new class of morphology-based restriction schemes that work as density filters; that is, the physical stiffness of an element is based on a function of the design variables of the neighboring elements. The new filters have the advantage that they eliminate grey scale transitions between solid and void regions. Using different test examples, it is shown that the schemes, in general, provide black and white designs with minimum length-scale constraints on either or both minimum hole sizes and minimum structural feature sizes. The new schemes are compared with methods and modified methods found in the literature.     

Bayesian image and pattern reconstruction from incomplete and noisy data

A Bayesian iterative method can be the basis for a wide range of technologies in the field of pattern recognition and image reconstruction. It involves finding the most probable solutions for images or patterns, if functionals describing the likelihood function and a priori information, respectively, are already known. The article describes the basic principles and recent advances in the development of BIM and its applications in various fields, mainly in tomography and restoration of functions from incomplete and noisy data.     

不完全投影数据图像重建的ART算法研究

ART算法是一种经典的图像重建方法,适合于不完全投影数据重建。为了提高重建速度,通常对权因子进行简化,其结果是在重建图像中普遍存在椒盐噪声。提出了一种改善重建质量的方法,在每次迭代后对重建图像进行有选择的平滑,将平滑结果作为下一次迭代的初值,其特点是将图像处理和图像重建相结合。仿真实验表明该方法非常有效,不但提高了重建质量,而且克服了利用Box模板进行平滑所造成的图像模糊现象。     

A modified medical image registration

Medical image registration is commonly used in clinical diagnosis, treatment, quality assurance, evaluation of curative efficacy and so on. In this paper, the edges of the original reference and floating images are detected by the B-spline gradient operator and then the binarization images are acquired. By computing the binarization image moments, the centroids are obtained. Also, according to the binarization image coordinates, the rotation angles of the reference and floating images are computed respectively, on the foundation of which the initial values for registering the images are produced. When searching the optimal geometric transformation parameters, the modified peak signal-to-noise ratio (MPSNR) is viewed as the similarity metric between the reference and floating images. At the same time, the simplex method is chosen as multi-parameter optimization one. The experimental results show that, this proposed method has a fairly simple implementation, a low computational load, a fast registration and good registration accuracy. It also can effectively avoid trapping in the local optimum and is adapted to both mono-modality and multi-modality image registrations. Also, the improved iterative closest point algorithm based on acquiring the initial values for registration from the least square method (LICP) is introduced. The experiments reveal that the measure acquiring the initial values for registration from image moments and the least square method (LSM) is feasible and resultful strategy.     

基于ROI的医学图像无损压缩

如何高效地压缩医学图像,以便减少存储空间和传输时间,已经成为迫切需要解决的问题。在分析现有的图像压缩方法和医学图像特点的基础上,针对医学图像有损压缩方法和无损压缩方法各自的不足,研究了一种基于ROI（Region of Interest）的医学图像无损压缩方法。