Narrow Results

In order to improve the performance of mass segmentation on mammograms, an intelligent algorithm is proposed in this paper. It establishes two mass models to characterize the various masses, and the ones in the denser tissue are represented with Model I, while the ones in the fatty tissue are represented with Model II. Then, it uses iterative thresholding to extract the suspicious area, as well as the rough regions of those masses matching Model II, and applies a DWT-based technique to locate those masses matching Model I, which are hidden in the high gray-level intensity and contrast area. A region growing process restricted by Canny edge detection is subsequently used to segment the rough regions of those masses matching Model I, and finally snakes are carried out to find all the mass regions roughly extracted above. Thirty patient cases with 60 mammograms and 107 masses were used for evaluation, and the experimental result has demonstrated the algorithm's better performance over the conventional methods.

This study has developed an object detection and segmentation technique for processing cytoplasm and cell nucleus on ThinPrep-cervical smear images at various magnifications. Both edge detection techniques and region growing for adaptive threshold were applied to a segment cell nucleus, a cytoplasm, and backgrounds using a cervical cell image.

To validate the accuracy and feasibility of the proposed method, we took a variety of cervical cell images to perform a series of experiments. The images were of superficial cells, intermediate cells, and abnormal cells, with each taken from ThinPrep smears at various magnifications. The results indicate that the proposed method can automatically segment cell nucleus and cytoplasm regions while accurately extracting object contours. These results can serve as a reference for examiners of cell pathologies.

After the first initiation, the Fuel Air Explosive (FAE) cloud formed through fuel explosion dispersal and it will generate tremendous damaging power after being detonated the second time. As the damaging power is closely related to the determination of reinitiation time, it is of great significance to study the growth principle of FAE cloud by means of analyzing FAE cloud images. Combining with background subtraction and region growing, an improved region growing image processing method was proposed, in which the seeds of region growing abstracted through background subtraction method and the growing criterion was modified. With this method, the integrate area of cloud can be obtained for extracting geometric parameters. Experiments were carried out on both cloudy and sunny days, and image overlap score was used to quantitatively evaluate the accuracy of images segmentation. The result indicated that this image processing method has advantages of high precision and robustness. In addition, the computation burden is reduced significantly compared with traditional region growing method.

Point cloud segmentation is a crucial fundamental step in 3D reconstruction, object recognition and scene understanding. This paper proposes a supervoxel-based point cloud segmentation algorithm in region growing principle to solve the issues of inaccurate boundaries and nonsmooth segments in the existing methods. To begin with, the input point cloud is voxelized and then pre-segmented into sparse supervoxels by flow constrained clustering, considering the spatial distance and local geometry between voxels. Afterwards, plane fitting is applied to the over-segmented supervoxels and seeds for region growing are selected with respect to the fitting residuals. Starting from pruned seed patches, adjacent supervoxels are merged in region growing style to form the final segments, according to the normalized similarity measure that integrates the smoothness and shape constraints of supervoxels. We determine the values of parameters via experimental tests, and the final results show that, by voxelizing and pre-segmenting the point clouds, the proposed algorithm is robust to noises and can obtain smooth segmentation regions with accurate boundaries in high efficiency.

The digital representation of an image requires a very large number of bits. The goal of image coding is to reduce this number, as much as possible, and to reconstruct a faithful duplicate of the original picture. Early efforts in image coding, solely guided by information theory, led to a plethora of methods. The compression ratio reached a plateau around 10: 1 a couple of years ago. Recent progress in the study of the brain mechanism of vision and scene analysis has opened new vistas in picture coding. Directional sensitivity of the neurones in the visual pathway combined with the separate processing of contours and textures has led to a new class of coding methods capable of achieving compression ratios as high as 100: 1. This paper presents recent progress on some of the main avenues of object-based methods. These second generation techniques make use of contour-texture modeling, new results in neurophysiology and psychophisics and scene analysis.

A method for improving the segmentation of images is presented. It involves taking an initial segmentation provided by some other means, and modifying the region boundaries depending on the estimated region models until an equilibrium is reached. The advantages of this technique are: (1) no parameters are required, (2) it is invariant under constant scalings of the image intensities, and (3) it is relatively insensitive to the position and topology of the initial segmentation. Examples are given of its application to single and multi-scale intensity images, textured images, range images and multi-band satellite images.

An unsupervised method to extract 2D and 3D inner earth structures from seismic reflection measurements is described. The application is a typical texture segmentation problem, which can be split up into a feature extraction stage and a segmentation stage. As a texture feature, the locally emergent frequency is estimated by a Gabor filter bank. The instantaneous frequency (IF) has already been successfully used for seismic trace analysis²¹ and will be compared with the results of the filter bank. The second stage of the algorithm involves a region-growing method to compute the final object structure. The extremely flexible segmentation scheme is appropriate for application to 2D and 3D images of arbitrary vectorial dimension. The merging decision is based on the mutual inlier ratio of two adjacent regions. This ratio is computed by robust regression techniques¹⁹ to avoid noise artifacts. A mutual inlier ratio discrimination function to recognize identical Gaussian distributions, guaranteeing a 97.5% certainty, is derived. This method is compared with the Kolmogorov–Smirnov test and results of the application in a segmentation algorithm are shown. The segmentation stage is also tested with different benchmark data sets from other computer vision problems to demonstrate its general flexibility.

Region of interest (ROI) is the most important part of an image that expresses the effective content of the image. Extracting regions of interest from images accurately and efficiently can reduce computational complexity and is essential for image analysis and understanding. In order to achieve the automatic extraction of regions of interest and obtain more accurate regions of interest, this paper proposes Optimized Automatic Seeded Region Growing (OASRG) algorithm. The algorithm uses the affinity propagation (AP) clustering algorithm to extract the seeds automatically, and optimizes the traditional region growing algorithm by regrowing strategy to obtain the regions of interest where target objects are contained. Experimental results show that our algorithm can automatically locate seeds and produce results as good as traditional region growing with seeds selected manually. Furthermore, the precision is improved and the extraction effect is better after the optimization with regrowing strategy.

Mammography imaging is one of the most successful techniques for breast cancer screening and detecting breast lesions. Detection of the Region of Interest (ROI) (where the possible abnormalities could be present) is the backbone for the success of any Computer-Aided Detection or Diagnosis (CADx) system. In this paper, to assist the CADx system, one computational model is proposed to detect breast mass lesions from mammogram images. At the beginning of the process, pectoral muscles from the mammograms are removed as a pre-processing step. Then by applying an automatic thresholding scheme with the required image processing techniques, different regions of breast tissues are ranked to detect the possible suspected region to refine the further segmentation task. One seeded region growing approach is proposed with an automatic seed selection criterion to detect the suspected region to segment the ROI. The proposed model has very less user intervention as maximum of the parameters are computed automatically. To evaluate the performance of the proposed model, it is compared with four different methods with six different evaluation metrics viz., Jaccard & Dice co-efficient, relative error, segmentation accuracy, error and Fowlkes–Mallows index (FMI). On the proposed model, 57 mammogram images are tested, consisting of four different cases that are collected from the publicly available benchmark database. The qualitative and quantitative analyses are performed to evaluate the proposed model. The best dice co-efficient, Jaccard co-efficient, accuracy, error and FMI values observed are 0.9506, 0.9471, 95.62%, 4.38% and 0.932, respectively. The superiority of the model over six state-of-the-art compared methods is well evident from the experimental results.

In this paper, a methodological approach to the segmentation of tumours skin lesions in dermoscopy images is presented. Melanoma is the most malignant skin tumor, growing in melanocytes, the cells responsible for pigmentation. This type of cancer is nowadays increasing rapidly, its related mortality rate increases by more modest and inversely proportional to the thickness of the tumor. This rate can be decreased by an earlier detection and better prevention. In dermatoscopic images, the segmentation is essential to characterize the information shape of the lesion and also to locate the tumor for analysis. In this domain, we have evaluated several techniques for the segmentation of dermatoscopic images. All these methods do not exactly separate the lesion from the background. In this work a fast approach in border detection of dermoscopy pigmented skin lesions images based on the region growing algorithm is presented. This method is tested on a set of 60 dermoscopy images. The obtained results show that the presented method achieves both fast and accurate border detection.

In the brain, the abnormal growth of cells or solid intracranial neoplasm is known as brain tumor, which is one of the world’s most tedious diseases. Hence, there is a need for segmentation and classification of the brain tumor accurately. It is difficult to separate the tumor tissues and other tissues from the brain. The major aim of this research is to use magnetic resonance imaging (MRI) segment and classify the brain tumor and all the abnormalities in the brain. The MRI is initially fed into the pre-processing system and then it is segmented using the region-growing segmentation algorithm in the pre-operative MRI. It produces the segmented area and it is forwarded for classification. In the classification step, the Honey Badger Algorithm (HBA) is applied to train the U-Net classifier. The tumor tissues and the different types of tissues or abnormalities in brain tumors are classified by this algorithm. Overall, the post-operative and pre-operative MRI brain tumor segmentation and classification consist of the same steps. To find out the pixel changes, both the segmented output of pre-operative and post-operative MRI was compared. It helps in finding the emerging tumor after surgery and the success rate of surgery. Based on pre-operative MRI, the implemented scheme has maximum specificity, sensitivity, and accuracy of 0.977, 0.968, and 0.949.

This article presents a novel method for activation detection in task-related functional magnetic resonance imaging (fMRI) based on the Empirical Mode Decomposition (EMD) algorithm. The basic concept stems mainly from the idea that the EMD performs well in isolating the imbedded stimulus from the activated Blood Oxygen Level Dependent (BOLD) signal. The power of the proposed method was compared with the General Linear Model (GLM), spatial Independent Component Analysis (ICA) and Region Growing (RG) methods on simulated and real datasets. Experimental results suggest an almost identical performance for the proposed method compared with the standard approach of fMRI signal detection (the GLM), which indicates that it is to become a viable alternative to fMRI analysis.

Automatic target tracking is a challenging task in video surveillance applications. Here, an offline target-tracking system in video sequences using Discrete Wavelet Transform is presented. The proposed algorithm uses co-occurrence features, derived from sub-bands of discrete wavelet transformed sub-blocks, obtained from individual video frames, to identify a seed in the frame. Then, the region-growing algorithm is applied to detect and track the target. The results of the proposed target detection and tracking system in video sequences are found to be satisfactory. The effectiveness of the target-tracking algorithm has been proved as the target gets detected, irrespective of size of the target, perspective view and cluttered environment.

Generally, leukemia is one of the blood cancers that can lead to death. A huge amount of immature WBCs in the bone marrow affect the healthy cells and this has become the leading cause of leukemia disease. Moreover, “Acute Lymphoblastic Leukemia (ALL)” is a type of blood cancer that is generally categorized with a huge amount of immature lymphocytes that are given as blast cells. But, the analysis of this model is dependent, boring as well as time-consuming based on the skills of hematologists. So, there is a requirement for the most desirable techniques to tackle these restrictions. Additionally, the accurate and automated diagnosis of ALL is a crucial task. In this case, an automated detection model of ALL using ensemble segmentation and a heuristic-assisted layer-improved hybrid deep learning approach is implemented. In the initial stage, the raw images are collected from benchmark datasets. Further, the image pre-processing is undergone by contrast enhancement and filtering process. After pre-processing, the image segmentation is carried out by ensemble segmentation, which is acquired by the region growing, K-medoids, and Fuzzy C-Means (FCM). From the segmented images, based on the mutual information, the pixel is selected optimally, in which the optimal pixel is identified by Opposition-based Rain Optimization Algorithm (OROA). Subsequently, the optimal pixels are fed as input to the Layer Improved Hybrid DenseNet and ResNet (LIH-DRNet) model that is constructed with DenseNet and ResNet, where some hyperparameters are tuned optimally by the improved ROA. At last, the performance is evaluated with diverse performance metrics. Thus, the findings reveal that the developed hybrid deep learning method achieves a higher detection rate to ensure the effectiveness of the model.

Medical image segmentation is one of the most productive research areas in medical image processing. The region growing algorithm is the most popular and efficient approach in this field. The key point of this method is to define the homogeneity criterion which is difficult to clearly describe and highly relied to the experience of the researcher. In this paper, we proposed an adaptive region growing algorithm which used gaussian mixture model (GMM) to describe the homogeneity and image shape properties of the local region in the medical image. Firstly, some statistical parameters are estimated by investigating the characteristics in the local region. Then these parameters are obtained by analyzing the statistical information using certain cluster algorithm. Lastly the growing parameters are automatically computed and applied in the region growing algorithm. To reduce the affection of the noise, we employ the anisotropic and multi-slices gaussian filter in the image processing. This method was tested with many CT and MR images. The experimental results show the approach is reliable and efficient.

An unsupervised method to extract 2D and 3D inner earth structures from seismic reflection measurements is described. The application is a typical texture segmentation problem, which can be split up into a feature extraction stage and a segmentation stage. As a texture feature, the locally emergent frequency is estimated by a Gabor filter bank. The instantaneous frequency (IF) has already been successfully used for seismic trace analysis²¹ and will be compared with the results of the filter bank. The second stage of the algorithm involves a region-growing method to compute the final object structure. The extremely flexible segmentation scheme is appropriate for application to 2D and 3D images of arbitrary vectorial dimension. The merging decision is based on the mutual inlier ratio of two adjacent regions. This ratio is computed by robust regression techniques¹⁹ to avoid noise artifacts. A mutual inlier ratio discrimination function to recognize identical Gaussian distributions, guaranteeing a 97.5% certainty, is derived. This method is compared with the Kolmogorov–Smirnov test and results of the application in a segmentation algorithm are shown. The segmentation stage is also tested with different benchmark data sets from other computer vision problems to demonstrate its general flexibility.

We discuss ways by which different methodologies for image analysis may be combined for better results. We focus on the combination of region growing and edge detection to achieve better segmentation…