Binary medical image compression using the volumetric run-length approach

Image compression has become an inevitable tool along with the advancing medical data acquisition and telemedicine systems. The run-length encoding (RLE), one of the most effective and practical lossless compression techniques, is widely used in two-dimensional space with common scanning forms such as zigzag and linear. In this study, an algorithm which takes advantage of the potential simplicity of the run-length algorithm is devised in a volumetric approach for three-dimensional (3D) binary medical data. The proposed algorithm, namely 3D-RLE, being different from the two-dimensional approach that utilizes only intra-slice correlations, is designed to compress binary volumetric data by employing also the inter-slice correlation between the voxels. Furthermore, it is extended to several scanning forms such as Hilbert and perimeter to determine an optimal scanning procedure coherent with the morphology of the segmented organ in data. The algorithm is employed on four datasets for a comprehensive assessment. Numerical simulation results demonstrated that the performance of the algorithm is 1:30 better than those of the state-of-the-art techniques, on average.     

Attenuating noise from computed tomography medical images using a coefficients‐driven total variation denoising algorithm

The aim of image denoising is to recover a visually accepted image from its noisy observation with as much detail as possible. The noise exists in computed tomography images due to hardware errors, software faults and/or low radiation dose. Because of noise, the analysis and extraction of accurate medical information is a challenging task for specialists. Therefore, a novel modification on the total variational denoising algorithm is proposed in this article to attenuate the noise from CT images and provide a better visual quality. The newly developed algorithm can properly detect noise from the other image components using four new noise distinguishing coefficients and reduce it using a novel minimization function. Moreover, the proposed algorithm has a fast computation speed, a simple structure, a relatively low computational cost and preserves the small image details while reducing the noise efficiently. Evaluating the performance of the proposed algorithm is achieved through the use of synthetic and real noisy images. Likewise, the synthetic images are appraised by three advanced accuracy methods –Gradient Magnitude Similarity Deviation (GMSD), Structural Similarity (SSIM) and Weighted Signal‐to‐Noise Ratio (WSNR). The empirical results exhibited significant improvement not only in noise reduction but also in preserving the minor image details. Finally, the proposed algorithm provided satisfying results that outperformed all the comparative methods.     

A minute lossy method for 2D‐gel images compression

This article presents a compression method to encode a 2D‐gel image by using hybrid lossless and lossy techniques. In this method, areas containing protein spots are encoded using lossless method while the background is encoded using the lossy method. A 2D‐gel image usually covers a large portion of the background in which has colors that are close to white. The VQ codebook‐generating approach gives more codewords to describe the background; consequently, the proposed method can nearly precisely depict the background of the 2D‐gel image and exactly record protein spots without any losses. Therefore, it can provide a high compression ratio. Image compressed by this method can nearly be lossless reconstructed. The experimental results show that the compression ratio is significantly improved with acceptable image quality compared to the JPEG‐LS method. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 1–8, 2006     

分块K-LT及查表量化编码的彩色图像压缩方法

针对卡洛变换(K-LT)应用于彩色图像压缩时因额外码流开销大而导致编/解码速度慢的缺点,提出了一种最大限度地减少额外码流开销的彩色图像压缩方法:先将RGB色彩空间的图像转换到YCbCr色彩空间采样,初步去除各颜色分量间的相关性;然后合并各色彩分量,进一步利用分块K-LT去除分量内部的信息相关性;最后对K-LT的重要系数采取查表的策略进行量化和哈夫曼编码.分块K-LT大幅度降低了向量空间的维数及变换矩阵的维数,减少了计算量和额外码流的存储开销;而查表提取K-LT重要特征系数的策略进一步节省了码流的存储空间.实验结果表明,该方法不仅编/解码速度快,而且相同压缩比下的峰值信噪比明显高于JPEG2000压缩方法.     

REPro.JPEG: a new image compression approach based on reduction/expansion image and JPEG compression for dermatological medical images

Medical images are known for their huge volume which becomes a real problem for their archiving or transmission notably for telemedicine applications. In this context, we present a new method for medical image compression which combines image definition resizing and JPEG compression. We baptise this new protocol REPro.JPEG (reduction/expansion protocol combined with JPEG compression). At first, the image is reduced then compressed before its archiving or transmission. At last, the user or the receiver decompresses the image then enlarges it before its display. The obtain results prove that, at the same number of bits per pixel lower than 0.42, that REPRo.JPEG guarantees a better preservation of image quality compared to the JPEG compression for dermatological medical images. Besides, applying the REPRo.JPEG on these colour medical images is more efficient while using the HSV colour space compared to the use of RGB or YCbCr colour spaces.     

3D multiwavelet based block coding algorithm for compression of volumetric medical images

Three dimensional (3D) medical images possess some specific characteristics that should be utilized by an efficient compression scheme. In this article, one such compression scheme for volumetric 3D medical image data is presented. Two processes involved in this scheme are decorrelation and encoding. Decorrelation of the 3D data is realized through 3D multiwavelet transform with apt prefiltering so as to give good representation of the image which could be exploited by the encoder. Encoding is done through proposed Block Coding Algorithm, which is embedded, block based, and wavelet transform coding algorithm without maintaining any list structures. The idea behind this algorithm is to sort the 3D transform coefficients in to a 1D array with respect to declining thresholds and to use state table to keep track of the blocks and coefficients that has been coded. In the experiment conducted on various 3D magnetic resonance and computed tomography images of human brain with multiwavelets such as Geronimo–Hardin–Massopust, Chui‐Lian, and orthogonal symmetric/antisymmetric (SA4), efficiency of the proposed scheme was weighed against the state of art encoders such as 3D Set Partitioning in Hierarchical Trees, 2D Set Partitioned Embedded BloCK Coder, and No List SPIHT. Attributes used for performance measurements are peak signal to noise ratio, bit rate, and structural similarity index of reconstructed image with respect to original image. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 182–192, 2014     

X‐ray computed tomography quantification of damage in concrete under compression considering irreversible mode‐II microcracks

A method for X‐ray computed tomography quantification of damage in concrete under compression considering irreversible mode‐II microcracks is developed. To understand damage behaviour in concrete, a micromechanical analysis of damage under biaxial compression is conducted focusing on random micro‐defects in micro‐cells isolated from the representative volume element. Furthermore, for stress–strain response prediction, a quantification is developed concerning the behaviours of the dominant macrocracks in multiaxial compression. Specifically, two crack types are taken into account: mode‐I cracks and irreversible deformation cracks (including mode‐II microcracks). Furthermore, mode‐I cracks generate compression‐induced tensile load (transverse) area reduction and further stiffness degradation, whereas the latter contribute to the development of irreversible strains. Additionally, by investigating the development of gradually converging dominant cracks, the procedure for quantifying damage is competently executed. In addition, distinguished from other approaches, the quantified damage can be applied directly to constitutive models to produce stress–strain response highly agrees with experimental results.     

IGM‐based perceptual multimodal medical image fusion using free energy motivated adaptive PCNN

Multimodal medical image fusion merges two medical images to produce a visual enhanced fused image, to provide more accurate comprehensive pathological information to doctors for better diagnosis and treatment. In this article, we present a perceptual multimodal medical image fusion method with free energy (FE) motivated adaptive pulse coupled neural network (PCNN) by employing Internal Generative Mechanism (IGM). First, source images are divided into predicted layers and detail layers with Bayesian prediction model. Then to retain human visual system inspired features, FE is used to motivate the PCNN for processing detail layers, and large firing times are selected as coefficients. The predicted layers are fused with the averaging strategy as activity level measurement. Finally, the fused image is reconstructed by merging coefficients in both fused layers. Experimental results visually and quantitatively show that the proposed fusion strategy is superior to the state‐of‐the‐art methods.     

Multimodal sensor medical image fusion based on mutual‐structure for joint filtering using sparse representation

Multimodal sensor medical image fusion has been widely reported in recent years, but the fused image by the existing methods introduces low contrast information and little detail information. To overcome this problem, the new image fusion method is proposed based on mutual‐structure for joint filtering and sparse representation in this article. First, the source image is decomposed into a series of detail images and coarse images by mutual‐structure for joint filtering. Second, sparse representation is adopted to fuse coarse images and then local contrast is applied for fusing detail images. Finally, the fused image is reconstructed by the addition of the fused coarse images and the fused detail images. By experimental results, the proposed method shows the best performance on preserving detail information and contrast information in the views of subjective and objective evaluations.     

Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.     

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Recently, the computed tomography (CT) and magnetic resonance imaging (MRI) medical image fusion have turned into a challenging issue in the medical field. The optimal fused image is a significant component to detect the disease easily. In this research, we propose an iterative optimization approach for CT and MRI image fusion. Initially, the CT and MRI image fusion is subjected to a multilabel optimization problem. The main aim is to minimize the data and smoothness cost during image fusion. To optimize the fusion parameters, the Modified Global Flower Pollination Algorithm is proposed. Here, six sets of fusion images with different experimental analysis are evaluated in terms of different evaluation metrics such as accuracy, specificity, sensitivity, SD, structural similarity index, feature similarity index, mutual information, fusion quality, and root mean square error (RMSE). While comparing to state‐of‐art methods, the proposed fusion model provides best RMSE with higher fusion performance. Experiments on a set of MRI and CT images of medical data set show that the proposed method outperforms a very competitive performance in terms of fusion quality.     

Contrast enhancement of computed tomography images by adaptive histogram equalization‐application for improved ischemic stroke detection

The visualization of computed tomography brain images is basically done by performing the window setting, which stretches an image from the Digital Imaging and Communications in Medicine format into the standard grayscale format. However, the standard window setting does not provide a good contrast to highlight the hypodense area for the detection of ischemic stroke. While the conventional histogram equalization and other proposed enhanced schemes insufficiently enhance the image contrast, they also may introduce unwanted artifacts on the so‐called “enhanced image.” In this article, a new adaptive method is proposed to excellently improve the image contrast without causing any unwanted defects. The method first decomposed an image into equal‐sized nonoverlapped sub‐blocks. After that, the distribution of the extreme levels in the histogram for a sub‐block is eliminated. The eliminated distribution pixels are then equally redistributed to the other grey levels with threshold limitation. Finally, the grey level reallocation function is defined. The bilinear interpolation is used to estimate the best value for each pixel in the images to remove the potential blocking effect. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 153–160, 2012     

An adaptive dynamic combined energy minimization model for few‐view computed tomography reconstruction

Recently, the potential harm of electromagnetic radiation used in computed tomography (CT) scanning has been paid much attention to. This makes the few‐view CT reconstruction become an important issue in medical imaging. In this article, an adaptive dynamic combined energy minimization model is proposed for few‐view CT reconstruction based on the compress sensing theory. The L2 energy of the image gradient and the total variation (TV) energy are combined, and the working regions of them are separated adaptively with a dynamic threshold. With the proposed model, the TV model's disadvantageous tendency of uniformly penalize the image gradient irrespective of the underlying image structures is overcome. Numerical experiments of the proposed model are performed with various insufficient data problems in fan‐beam CT and suggest that both the reconstruction speed and quality of the results are generally improved. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 44–52, 2013.     

Development of a three-dimensional finite element model for a unidirectional carbon fiber reinforced plastic based on X-ray computed tomography images and the numerical simulation on compression

A unidirectional carbon fiber reinforced plastic (CFRP) was scanned by an X-ray computed tomography (CT) system. Based on the X-ray CT images, a three-dimensional model with random fiber waviness was developed. Each fiber location was identified in a sectional CT image. Subsequently, the relative displacement of fibers between adjacent sectional CT images was obtained with a digital image correlation method. This procedure was repeated to obtain fiber waviness along the axial direction. The constructed three-dimensional fiber model showed random waviness of each fiber in the unidirectional CFRP. Finite element analysis was performed using the three-dimensional model. Simulation results showed bending and twisting deformations coupled with axial contractions during axial compression, which developed due to fiber waviness. A reduction of the fiber directional Young’s modulus due to fiber random waviness was quantitatively evaluated.     

Content‐based gastric image retrieval using convolutional neural networks

The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content‐based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet‐18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet‐18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006 , outperforming other state‐of‐art conventional methods. Furthermore, we constructed a Gastric‐Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis.     

Segmentation for brain magnetic resonance images using dual‐tree complex wavelet transform and spatial constrained self‐organizing tree map

In brain MR images, the noise and low‐contrast significantly deteriorate the segmentation results. In this paper, we introduce a novel application of dual‐tree complex wavelet transform (DT‐CWT), and propose an automatic unsupervised segmentation method integrating DT‐CWT with self‐organizing map for brain MR images. First, a multidimensional feature vector is constructed based on the intensity, low‐frequency subband of DT‐CWT, and spatial position information. Then, a spatial constrained self‐organizing tree map (SCSOTM) is presented as the segmentation system. It adaptively captures the complicated spatial layout of the individual tissues, and overcomes the problem of overlapping gray‐scale intensities for different tissues. SCSOTM applies a dual‐thresholding method for automatic growing of the tree map, which uses the information from the high‐frequency subbands of DT‐CWT. The proposed method is validated by extensive experiments using both simulated and real T1‐weighted MR images, and compared with the state‐of‐the‐art algorithms. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 208–214, 2014     

An improved label fusion approach with sparse patch‐based representation for MRI brain image segmentation

The multi‐atlas patch‐based label fusion (LF) method mainly focuses on the measurement of the patch similarity which is the comparison between the atlas patch and the target patch. To enhance the LF performance, the distribution probability about the target can be used during the LF process. Hence, we consider two LF schemes: in the first scheme, we keep the results of the interpolation so that we can obtain the labels of the atlas with discrete values (between 0 and 1) instead of binary values in the label propagation. In doing so, each atlas can be treated as a probability atlas. Second, we introduce the distribution probability of the tissue (to be segmented) in the sparse patch‐based LF process. Based on the probability of the tissue and sparse patch‐based representation, we propose three different LF methods which are called LF‐Method‐1, LF‐Method‐2, and LF‐Method‐3. In addition, an automated estimation method about the distribution probability of the tissue is also proposed. To evaluate the accuracy of our proposed LF methods, the methods were compared with those of the nonlocal patch‐based LF method (Nonlocal‐PBM), the sparse patch‐based LF method (Sparse‐PBM), majority voting method, similarity and truth estimation for propagated segmentations, and hierarchical multi‐atlas LF with multi‐scale feature representation and label‐specific patch partition (HMAS). Based on our experimental results and quantitative comparison, our methods are promising in the magnetic resonance image segmentation. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 23–32, 2017     

Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed‐based region growing algorithm

Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed‐based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 ‐ w, T2 – w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C – Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO‐based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO‐based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 33–45, 2017     

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type‐2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard T animoto C oefficient Index. The proposed CBS combined with interval type‐2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph‐cut based, and self‐organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology.