Region Growing Segmentation Technique Research Articles

Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

Medical image classification becomes a vital part of the design of computer aided diagnosis (CAD) models. The conventional CAD models are majorly dependent upon the shapes, colors, and/or textures that are problem oriented and exhibited complementary in medical images. The recently developed deep learning (DL) approaches pave an efficient method of constructing dedicated models for classification problems. But the maximum resolution of medical images and small datasets, DL models are facing the issues of increased computation cost. In this aspect, this paper presents a deep convolutional neural network with hierarchical spiking neural network (DCNN-HSNN) for medical image classification. The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images. In addition, region growing segmentation technique is involved to determine the infected regions in the medical image. Moreover, NADAM optimizer with DCNN based Capsule Network (CapsNet) approach is used for feature extraction and derived a collection of feature vectors. Furthermore, the shark smell optimization algorithm (SSA) based HSNN approach is utilized for classification process. In order to validate the better performance of the DCNN-HSNN technique, a wide range of simulations take place against HIS2828 and ISIC2017 datasets. The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures. Please type your abstract here.

FUZZY BASED REGION GROWING SEGMENTATION WITH MODIFIED LEAST SQUARE FITTING ALGORITHM FOR AUTOMATIC FETAL GROWTH ESTIMATION

During pregnancy monitoring, the fetal ultrasound image can provide valuable diagnostic information. Due to low soft tissue contrast, speckle, and shadowing effects, manual segmentation of subcortical structures in ultrasonic volumes is time-consuming and difficult. For this reason, we developed Discrete Wavelet Transform (DWT) based fuzzy region growing segmentation scheme. Considering the aforementioned facts, the present study deals with automatic estimation of gestation age using the head circumference data obtained/ retrieved from the ultrasonic images of the fetus. The proposed work utilizes the DWT based anisotropic diffusion filtering method to enhance the de-noised image resolution. Further implementation of genetic algorithm based Least Square Fitting (LSF) algorithm to find bi-parietal distance (BPD) and head circumference (HC) which will enable the calculate of gestation age. The fetal age is automatically estimated using K-Nearest Neighbour (KNN) Classifier from the standard reference values. By deploying fuzzy based region growing segmentation technique this study attempts to aid unique and effective segmentation process. Apart from highlighting the uniqueness of this method, from other existing techniques, this study elaborately discusses the steps and mechanisms involved in various stages of image processes starting from effective denoising to fetal age determination.

Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

Diabetic Retinopathy (DR) has become a widespread illness among diabetics across the globe. Retinal fundus images are generally used by physicians to detect and classify the stages of DR. Since manual examination of DR images is a time-consuming process with the risks of biased results, automated tools using Artificial Intelligence (AI) to diagnose the disease have become essential. In this view, the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification (ODL-FDRDC) technique. The intention of the proposed ODL-FDRDC technique is to identify DR and categorize its different grades using retinal fundus images. In addition, ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions. Moreover, the fusion of two DL models namely, CapsNet and MobileNet is used for feature extraction. Further, the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm (COA). Gated Recurrent Unit (GRU) is also utilized to identify DR. The experimental results of the analysis, accomplished by ODL-FDRDC technique against benchmark DR dataset, established the supremacy of the technique over existing methodologies under different measures.

Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT.

High mortality rate due to liver cirrhosis has been reported over the globe in the previous years. Early detection of cirrhosis may help in controlling the disease progression toward hepatocellular carcinoma (HCC). The lack of trained CT radiologists and increased patient population delays the diagnosis and further management. This study proposes a computer-aided diagnosis system for detecting cirrhosis and HCC in a very efficient and less time-consuming approach. Contrast-enhanced CT dataset of 40 patients (n = 40; M:F = 5:3; age = 25-55years) with three groups of subjects: healthy (n = 14), cirrhosis (n = 12) and cirrhosis with HCC (n = 14), were retrospectively analyzed in this study. A novel method for the automatic 3D segmentation of liver using modified region-growing segmentation technique was developed and compared with the state-of-the-art deep learning-based technique. Further, histogram parameters were calculated from segmented CT liver volume for classification between healthy and diseased (cirrhosis and HCC) liver using logistic regression. Multi-phase analysis of CT images was performed to extract 24 temporal features for detecting cirrhosis and HCC liver using support vector machine (SVM). The proposed method produced improved 3D segmentation with Dice coefficient 90% for healthy liver, 86% for cirrhosis and 81% for HCC subjects compared to the deep learning algorithm (healthy: 82%; cirrhosis: 78%; HCC: 70%). Standard deviation and kurtosis were found to be statistically different (p < 0.05) among healthy and diseased liver, and using logistic regression, classification accuracy obtained was 92.5%. For detecting cirrhosis and HCC liver, SVM with RBF kernel obtained highest slice-wise and patient-wise prediction accuracy of 86.9% (precision = 0.93, recall = 0.7) and 80% (precision = 0.86, recall = 0.75), respectively, than that of linear kernel (slice-wise: accuracy = 85.4%, precision = 0.92, recall = 0.67; patient-wise: accuracy = 73.33%, precision = 0.75, recall = 0.75). The proposed computer-aided diagnosis system for detecting cirrhosis and hepatocellular carcinoma (HCC) showed promising results and can be used as effective screening tool in medical image analysis.

Temperature profile guided segmentation for detection of early subclinical inflammation in arthritis knee joints from thermal images

In arthritis, subclinical inflammation referred to the clinical condition when rheumatologists are in confusion about the presence of inflammation using clinical and pathological observations. Application of Thermal imaging in detection of subclinical inflammation is highlighted in this literature. Segmentation of the hotspot area from the thermal image is the initial step for further analysis of the hotspot. Analysis of the hotspot will help in prediction of the subclinical inflammation, impact of inflammation. Methodologies reported in existing literature for segmentation of hotspot or inflamed knee region in medical thermal images suffer from over and under extraction.In the present scope, we try to overcome this limitation by extending the conventional region growing segmentation technique with stronger similarity criteria and stopping rule. In this method, hotspot or inflamed region is generated by taking the intersection of two independent regions produced by two different version of Region growing algorithm using a separate set of parameters. An automatic multiseed selection procedure ensures prevention of missed segmentation. We validate our technique by experimentation on various thermal image datasets like a newly created inflammatory thermal knee-joint-Database of 50 images, DBT-TU-JU Dataset, and DMR-IR Dataset. The effectiveness of the proposed technique is established compared to the performance of state-of-the-art competing methodologies.

Accurate segmentation of inflammatory and abnormal regions using medical thermal imagery.

Methodologies reported in the existing literature for identification of a region of interest (ROI) in medical thermograms suffer from over- and under-extraction of the abnormal and/or inflammatory region, thereby causing inaccurate diagnoses of the spread of an abnormality. We overcome this limitation by exploiting the advantages of a logarithmic transformation. Our algorithm extends the conventional region growing segmentation technique with a modified similarity criteria and a stopping rule. In this method, the ROI is generated by taking common information from two independent regions produced by two different versions of a region-growing algorithm that use different parameters. An automatic multi-seed selection procedure prevents missed segmentations in the proposed approach. We validate our technique by experimentation on various thermal images of the inflammation of affected knees and abnormal breasts. The images were obtained from three databases, namely the Knee joint dataset, the DBT-TU-JU dataset, and the DMR-IR dataset. The superiority of the proposed technique is established by comparison to the performance of state-of-the-art competing methodologies. This study performed temperature emitted inflammatory area segmentation on thermal images of knees and breasts. The proposed segmentation method is of potential value in thermal image processing applications that require expediency and automation.

Shape-Based Building Detection in Visible Band Images Using Shadow Information

This paper introduces a novel methodology for automated detection of buildings from single high-resolution optical images with only visible red, green, and blue bands of data. In particular, we first investigate the shadow evidence to focus on building regions. Then, a novel Markov random field (MRF)-based region growing segmentation technique is proposed. Image is oversegmented into smaller homogeneous regions that can be used to replace the rigid structure of the pixel grid. An iterative classification merging is then applied over this set of regions. At each iteration, regions are classified using a region-level MRF model, then, according to the position of shadows, regions having the same class are merged to produce new regions whose shapes are appropriate to rectangles. The final buildings are determined using a recursive minimum bounding rectangle. The experimental results prove that the proposed method is applicable in various areas (high dense urban, suburban, and rural) and is highly robust and reliable.

An Efficient Image Segmentation and Classification of Tumors in Mammography Image using K-Means Clustering with ROI and ANN Classifier

Breast cancer is commonly occurred in women that causes of death. An early detection of breast cancer can be used to long survival of patients. The tumor is detected in breast using mammography X-ray image. This mammography is one of the special cases of CT scan that is utilized to discover the breast cancer. It is screening method to find the malignant tumor cells in breast among women at early stage to avoid the deaths of patient. However, the radiologists not forever provide accurate results and detection of malignant tumor in breast is a one of the challenging issue due to the tumor cells structure. Therefore, this paper presents K-means clustering with Probabilistic neural networks for improvement of mammogram image quality to provide accurate results. K- Means clustering is applied to segment the mammogram image efficiently and presents an automatic support system for image classification using trained probabilistic neural networks and Artificial Neural networks (ANN) classification will be employed to classify the stage of breast cancer tumor that is caring, malignant or standard. Describing the ROI (Region of Interest), utilizing Region growing segmentation technique, evaluating the area of assumed cancer and establish the image classification of the area on the mammogram image using Sample K-Means Clustering and PNN techniques. Gray Level Co-occurrence Matrix (GLCM) is to extract the features from the texture that features may be in the form of structure, location and outside etc. In this process, the preprocessing method is applied to reduce the noise from the mammogram image for the image enhancement. For the preprocessing, the filter methods are used to give image quality. The performance of k-means clustering and PNN techniques provides fast and accurate image segmentation and classification than the other techniques to detect tumor cells in breast at early stage efficiently.

A Masked-Watershed algorithm for segmentation of Brain tumour MR Images

imaging creates use of the technology to non- invasively make known the interior structure of the human body. By way of medical imaging modalities patient's life be able to be improved throughout an accurate and rapid remedial without any side effects. The most important reason of this paper is to increased methodology that may accurately classify a tumor growth from abnormal tissues. This examination work has distinguished region growing segmentation Technique and implement and reflect. From our validation test comes about, this examination work uncovered that this model neglects to create useful groups order force effect poor tumor characterization precision. The images used for tumor segmentation are obtained from MRI modality. During this research, we've developed an image segmentation technique based on catchments basins and ridge lines to segment the brain image accurately. This technique relies on interest techniques for extracting tumor portion of the images in the watershed segmentation. Direct application of watershed leads to over-segmentation because of the presence of noise and different irregularities accepted in digital images. Thus to avoid this, we've carried out some preprocessing to eliminate noise present within the MRI images through acquisition stage. Then mask the watershed segmentation. After preprocessing step, we have a tendency to calculate the morphological operation of the input images. This exists one of the real issues to predict the tumor design and to address this issue; the masked watershed Segmentation Technique is proposed and resolute overall Sensitivity, Specificity, and Accuracy. Meningiomas represent 36.4% of all primary brain tumors, creating them the foremost common primary brain tumor. There'll be an estimated 24,880 new cases in 2016.Gliomas, a broad expression which includes all tumors take place from the supportive tissue of the brain, correspond to 27% of the entire brain tumors and 80% of the entire malignant tumors. Glioblastomas represent 15.1% of the entire primary brain tumors and 55.1% of the entire gliomas. Glioblastoma has the maximum number of cases of all malignant tumors, with an estimated 12,120 original cases predicted in 2016. Astrocytomas, as well as glioblastoma, represent just about 75% of all gliomas. Nerve sheath tumors/acoustic neuromas characterize about 8% of all primary brain tumors. Pituitary tumors correspond to 15.5% of the entire primary brain tumors. There resolve to be an estimated 11,700 new cases of pituitary tumors in 2016. Lymphomas stand for 2% of all primary brain tumors. Oligodendrogliomas stand for nearly 2% of all primary brain tumors. Medulloblastomas/primitive tumors represent 1% of all primary brain tumors. The greater part of primary tumors (36.4%) is placed within the meninges. Possible usage for brain tumors differs fairly a bit stand on the size and position of the tumor in the brain, at the same time as the person's overall health and risk factors. Usually, on the other hand, the initial step is the removal of the tumor, generally through some type of surgical procedure. After that, chemotherapy, radiation therapy or a mixture of the two treatments is placed to eradicate cancer from neighboring tissue and hopefully prevent its repetition.

An Investigation of Image Segmentation Method for Remotely Sensed Hyperspectral Images with Region Object Aggregations

An important aspect of spectral image analysis is identification of materials present in the object or scene being imaged. Since multi-spectral or hyper spectral imagery is generally low resolution, it is possible for pixels in the image to contain several materials. A paramount issue in image processing area is to design and implement an efficient segmentation and classification techniques demanding optimal resources. This paper presents a survey on all prominent region growing segmentation techniques analyzing each one and thus sorting out an optimal and promising technique. Finally study the importance of the best merge region growing normally produces segmentations with closed connected region objects. Recognizing that spectrally similar objects often appear in spatially separate locations, present an approach for tightly integrating best merge region growing with nonadjacent region object aggregation, which we call hierarchical segmentation or HSeg. The effectiveness of the proposed methodology is illustrated by comparing its performance with the state-of-the-art methods on synthetic and real hyper spectral image data sets. The reported results give clear evidence of the relevance of using both spatial and spectral information in hyper spectral image segmentation.

Segmentation of Microscopic Images of Living Cells: A Study

In this paper, three different segmentation techniques are used, namely, region growing segmentation technique, watershed segmentation and texture segmentation, to accurately extract the shapes of membranes and nuclei from the inverted microscopic images, taken throughout the monolayer formation of BGM-70 (Baby Grevit Monkey) cell culture. This strategy is a prerequisite for an accurate quantitative analysis of cell shape and morphodynamics during organogenesis and is the basis for an integrated understanding of biological processes. The segmentation of cellular structures is achieved by first normalizing the microscopic images by performing CLAHE operation followed by de-noising by morphological opening and filling of the holes and then applying segmentation algorithm for cell shape reconstruction.

Image contrast enhancement through regional application of partitioned iterated function systems

A new technique is presented for enhancing the contrast in digital images, combining the theory of partitioned iterated function system (PIFS) and image segmentation. The image is first segmented through the region growing segmentation technique, and the PIFS enhancement algorithm is applied separately to each image segment. The defined PIFS of each section is modeled by a contractive transformation, which consists of an affine spatial transform, as well as the linear transform of the graylevels of image segment pixels. The transformation of the graylevels is determined by two parameters that adjust the brightness and contrast of the transformed image segment. After the PIFS algorithm is applied to each extracted image segment, a lowpass version of the original image is created. The contrast-enhanced image is obtained by suitably combining the original image with its lowpass version. The proposed regional PIFS approach was applied to numerous test images, ranging from medical data of various modalities to standard images. The obtained quantitative and qualitative results showed superior performance on behalf of the proposed method when compared with three other widely used contrast enhancement methods, namely, contrast stretching, unsharp masking, and contrast-limited adaptive histogram equalization.

Segmentation for Image Indexing and Retrieval on Discrete Cosines Domain

This paper used region growing segmentation technique to segment the Discrete Cosines (DC) image. The classic problem of content Based image retrieval (CBIR) is the lack of accuracy in matching between image query and image in the database. By using region growing technique on DC image,it reduced the number of image regions indexed. The proposed of recursive region growing is not new technique but its application on DC images to build indexing keys is quite new and not yet presented by many authors. T he experimental results show that the proposed methods on segmented images present good precision which are higher than 0.60 on all classes. So, it could be concluded that region growing segmented based CBIR more efficient compared to DC images in term of their precision 0.59 and 0.75, respectively. Moreover, DC based CBIR can save time and simplify algorithm compared to DCT images. The most significant finding from this work is instead of using 64 DCT coefficients this research only used 1/64 coefficients which is DC coefficient.

Survey on Region Growing Segmentation and Classification for Hyperspectral Images

Image processing of hyperspectral image sector shows a thriving upbeat in innovation of new and novel techniques. For obvious reasons, most of these apply to the process of image segmentation and classification, in which is the heart of image processing. Augmented use of hyperspectral images puts forth a hectic workload that needs to deal with spatial data imposing large memory and computing requirements. Thus, a paramount issue in image processing area is to design and implement segmentation and classification techniques demanding optimal resources. This paper presents a survey on all prominent region growing segmentation techniques analyzing each one and thus sorting out an optimal and promising technique.

Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis

To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach.

NDE weld defect detection and feature extraction using segmentation approach

In this paper, comparative study of morphological edge detection and region growing segmentation technique is introduced to detect and assess the flaws from the radiographic images. In the proposed method, segmentation algorithms are applied to detect the different types of flaws and calculate the necessary features such as major axis length, minor axis length, area, and perimeter. Computing time is optimised and algorithm is simpler to implement. These methodologies are compared and concluded to be effective for all possible nine types of weld flaws detection (slag inclusion, worm hole, porosity incomplete penetration, under cuts, cracks, lack of fusion, and weaving fault slag line). The experimental results show that our proposed method gives good performance for non-destructive evaluation (NDE) of radiographic images.

Detection of Flaws in Weldment Images Using Region Growing Segmentation Technique

This paper presents an efficient detection method to highlight the flaws present in weld images. The obtained results further help in finding the size and location o f the flaw. The method is applied on X-ray images having flaws generated during the welding process. Using region-growing method we merge the adjacent detected pixels on homogeneity criteria, to obtain the flaws. Region growing starts with seed(s). The seed value is determined with the help of histogram analysis. The peaks and valleys o f histogram help in determining the seed value. The developed algorithm is explained and applied on the welding images and some preliminary results are shown which are encouraging.

Morphological phase unwrapping

Phase unwrapping is the process of determining the absolute phase given its principal value. In this paper, we show that morphological image processing techniques can be helpful for solving this problem. The proposed procedure consists of three independent modules. The first aims at extracting a region of interest corresponding to the areas where interferences occur. This is achieved by processing the input interferograms with a combination of morphological opening and closing operators. The second finds the high boundaries of the wrapped fringes by enhancing them with a morphological gradient by erosion. Gaps along the fringe boundaries which would lead to errors in the unwrapped phase map are filled in by a morphological region growing segmentation technique. The third module consists in integrating the wrapped phase while taking into account the segmented fringes.