Top-hat Transform Research Articles

Small Infrared Target Detection via a Mexican-Hat Distribution

Although infrared small target detection has been broadly used in airborne early warning, infrared guidance, surveillance and tracking, it is still an open issue due to the low signal-to-noise ratio, less texture information, background clutters, and so on. Aiming to detect a small target in an infrared image with complex background clutters, this paper carefully studies the characteristics of a target in an IR image filtered by the difference of Gaussian filter, concluding that the intensity of the adjacent region around a small infrared target roughly has a Mexican-hat distribution. Based on such a conclusion, a raw infrared image is sequentially processed with the modified top-hat transformation and the difference of Gaussian filter. Then, the adjacent region around each pixel in the processed image is radially divided into three sub-regions. Next, the pixels that distribute as the Mexican-hat are determined as the candidates of targets. Finally, a real small target is segmented out by locating the pixel with the maximum intensity. Our experimental results on both real-world and synthetic infrared images show that the proposed method is so effective in enhancing small targets that target detection gets very easy. Our method achieves true detection rates of 0.9900 and 0.9688 for sequence 1 and sequence 2, respectively, and the false detection rates of 0.0100 and 0 for those two sequences, which are superior over both conventional detectors and state-of-the-art detectors. Moreover, our method runs at 1.8527 and 0.8690 s per frame for sequence 1 and sequence 2, respectively, which is faster than RLCM, LIG, Max–Median, Max–Mean.

Quantifying soot nanostructures: Importance of image processing parameters for lattice fringe analysis

Fringe analysis is a commonly used method to quantify soot nanostructures. However, the settings of the involved filters and their impact on the results are rarely addressed. In this study, the influence of the three filter parameters as well as two aspects of the image acquisition was assessed experimentally. For the analysis, a carbon black as well as one diesel engine and one gasoline direct injection (GDI) engine soot sample were used. Gaussian low-pass filter standard deviations larger 1.5 yielded only minor differences in fringe metrics. Standard deviations between 2.0 and 3.0 enabled realistic representation of fringes. A linear correlation was found between the white top-hat transformation disk size and all fringe analysis metrics. For realistic nanostructure representation, disk sizes of 5 px and 7 px are most suitable. Threshold values as calculated by Otsu's method generally yielded the best nanostructure representation. Any deviation distorted the extracted fringes and noticeably reduced their total number. Thus, consistent use of Otsu thresholds without alterations is advised. Deviating from the neutral electron microscope focus point by under- and over-focusing resulted in distinctive drops in both fringe lengths and Otsu thresholds. Consistent focusing with the help of fast Fourier transformations of the respective particles is vital for reliable results. The effect of reduced noise levels by repeated averaged images was found to be minor beyond the model of the camera used. The region of interest size correlated linearly with the number of extracted fringes, however, it did not affect the fringe metrics. For statistically reliable analysis, a minimum of 4000 fringes is suggested. The GDI sample exhibited the shortest fringes and the highest tortuosity. For diesel soot and carbon black, similar fringe lengths could be observed. The highest tortuosity was found for GDI soot, followed by diesel soot and carbon black.

Histopathological Image Classification Scheme for Breast Tissues to Detect Mitosis

Histopathological images are widely utilized by the pathologists to determine the severity of the cancer. Usually, the pathologists analyse histopathological images manually, which demands more time and effort. The quality of image interpretation can be improvised with the help of a computerized assisting system for image analysis. This idea enhances the accuracy of the system as the computerized system helps in making decisions, which the pathologist may recheck. Understanding the benefits, this article presents a histopathological image classification scheme meant for breast tissues in detecting the mitotic cells. The goal is achieved by preprocessing the image by performing top and bottom hat transformation followed by stain normalization. The nuclei are then located with the help of Localized Active Contour Model (LACM) combined with Lion Optimization (LO) algorithm. The shape, statistical and texture features are extorted from the areas of interest and the differentiation of mitotic cells from the nonmitotic ones is performed by Support Vector Machine (SVM) classifier. The work efficiency is tested with respect to the standard performance measures such as accuracy, sensitivity, specificity and time consumption.

Infrared and visible image fusion via hybrid decomposition of NSCT and morphological sequential toggle operator

Infrared and visible image fusion technique is beneficial to improve scene description capability and target detection accuracy for modern image processing. In this paper, we propose a novel and effective image enhanced fusion via a hybrid decomposition of non-subsample contourlet transform (NSCT) and morphological sequential toggle operator (MSTO). MSTO is constructed as a multi-scale decomposition on the base of top-hat transformation. We employ MSTO to extract the bright/dark image features (BIF/DIF) from approximation subband of NSCT decomposition. This hybrid decomposition can effectively suppress the noise and pseudo-edge of source images. The extracted BIF and DIF are fused with maximum selection rule based on local energy map at different scales. Meanwhile, the guided filter is used to enhance the fused BIF and DIF. These enhanced fused BIF and DIF are integrated into the combined approximation subband, which can largely improve the contrast and visible effect of final fusion image. Our experiments demonstrate that this proposed approach is superior to other fusion methods in terms of visual inspection and objective measures.

Detection of Hard Exudates in Retinopathy Images

The tissue layer located at the back of the eye is known as retina which converts the incoming light into nerve signals and those signals are sent to the brain for understanding. The damage onto the retina is termed as retinopathy and that may lead to vision weakening or vision loss. The hard exudates are small white or yellowish white deposits with their edges being clear and sharp. In the proposed methods we take color image of retina then extract the green channel of that image then apply top hat transformation and bottom hat transformation on that image. The DIARETDB1 and High-Resolution Fundus (HRF) databases are used for performance evaluation of the proposed method. The proposed technique achieves accuracy 97%, sensitivity 95%, and specificity 96% and it takes average 5.6135 second for detection of hard exudates in an image.

Automated segmentation of villi in histopathology images of placenta

PurposeManual analysis of clinical placenta pathology samples under the microscope is a costly and time-consuming task. Computer-aided diagnosis might offer a means to obtain fast and reliable results and also substantially reduce inter- and intra-rater variability. Here, we present a fully automated segmentation method that is capable of distinguishing the complex histological features of the human placenta (i.e., the chorionic villous structures). MethodsThe proposed pipeline consists of multiple steps to segment individual placental villi structures in hematoxylin and eosin (H&E) stained placental images. Artifacts and undesired objects in the histological field of view are detected and excluded from further analysis. One of the challenges in our new algorithm is the detection and segmentation of touching villi in our dataset. The proposed algorithm uses the top-hat transformation to detect candidate concavities in each structure, which might represent two distinct villous structures in close proximity. The detected concavities are classified by extracting multiple features from each candidate concavity. Our proposed pipeline is evaluated against manual segmentations, confirmed by an expert pathologist, on 12 scans from three healthy control patients and nine patients diagnosed with preeclampsia, containing nearly 5000 individual villi. The results of our method are compared to a previously published method for villi segmentation. ResultsOur algorithm detected placental villous structures with an F1 score of 80.76% and sensitivity of 82.18%. These values are substantially better than the previously published method, whose F1 score and sensitivity are 65.30% and 55.12%, respectively. ConclusionOur method is capable of distinguishing the complex histological features of the human placenta (i.e., the chorionic villous structures), removing artifacts over a large histopathology sample of human placenta, and (importantly) account for touching adjacent villi structures. Compared to existing methods, our developed method yielded high accuracy in detecting villi in placental images.

A Radiomic feature-based Nipple Detection Algorithm on Digital Mammography.

In the diagnosis and detection of breast lesions, the nipple is an important anatomical landmark which can be used for the registration on multiview mammograms. In this study, we propose a new detection algorithm for nipples on digital mammography (DM) by applying pixel classification based on geometric and radiomic features extracted from breast boundary regions. The imaging characteristics of nipples are closely related to the visibility on mammograms. To locate the nipple on mammogram, a searching area is first determined based on the breast boundary and chest wall orientation. Two different approaches are developed for obvious and subtle nipples, respectively. For obvious nipples, top hat transformation is employed to detect the nipple region, whose geometric center is regarded as the nipple position. For subtle nipples, the curved searching area near the breast boundary is mapped onto a Cartesian plane through a revised rubber band straightening transformation. On the straightened searching area, the geometric and radiomic features are calculated along the normal direction of the breast boundary, and a random forest classifier is trained for subtle nipple localization. Seven hundred and twenty-one DMs were collected for the evaluation of the proposed algorithm. The locations of nipples are manually identified by an experienced radiologist as the reference standard. The average Euclidean distance between the computed nipple position and the reference standard was 2.69mm (obvious) and 7.81mm (subtle), respectively. A total of 97.61% of the obvious nipples (613/628) and 88.17% of the subtle nipples (82/93) were detected within a 10-mm radius centered from the reference standard. The evaluation results show that the proposed method is effective for nipple detection on DM, especially for subtle nipple detection.

Effect Analysis of Optical Masking Algorithm for GEO Space Debris Detection

A method with high detection rate, low false-alarm rate, and low computational cost is presented for removing stars and noise and detecting space debris with signal-to-noise ratio (SNR>3) in consecutive raw frames. The top-hat transformation is implemented firstly to remove background, then a masking technique is proposed to remove stars, and finally, a weighted algorithm is used to detect the pieces of space debris. The simulation samples are images taken by 600 mm ground-based telescope. And a series of simulation targets are added to the image in order to test the detection rate and false-alarm rate of different SNRs. The telescope in this paper is worked in “staring target mode.” The experimental results show that the proposed method can detect space debris effectively with low false-alarm by only three frames. When the SNR is higher than 3, the detection probability can reach 94%, and the false-alarm rate is below 2%. The running time of this algorithm is within 1 second. Additionally, algorithm performance tests in different regions are also carried out. A large set of image sequences are tested, which proves the stableness and effectiveness of the proposed method.

Detection of Cracked Digitized Paintings and Manuscripts Based on Threshold Techniques

Ancient paintings are cultural heritages that requireextensive and intricate preservation undertakings. With the passage of time, paintings can be damaged, and common deteriorations found in the ancient paintings include cracking. Cracks can be caused by many factors, such as aging, drying, and mechanical factors. Fortunately, digital images of ancient paintings can be restored using image processing techniques. This work involved with the development of an automatic crack detection system. First, the binarization technique was applied to the digital images that allowed for the extraction of cracks from the background of these images, based on the thresholding segmentation technique after using the top-hat transformation technique during pre-processing. Thin dark patterns that were misidentified as cracks were then automatically removed. This was then followed by the crack filling via order statistic algorithm. The main contribution of this paper is a simple and robust monochrome method that can be used to restore digital images of historical paintings and manuscripts.

Asymmetric post-spreading magmatism in the South China Sea: based on the quantification of the volume and its spatiotemporal distribution of the seamounts

ABSTRACT The volume of seamounts is an essential indicator of the intensity of mantle convection during the evolution of the oceanic lithosphere. Drilling and dredging samples suggest volcanic seamounts are widely distributed in the oceanic basin of the South China Sea, and most of them were formed after cessation of seafloor spreading. By using an SRTM15_PLUS Digital Elevation Model with a 15-arc-second grid, we developed a spatial filtering method based on the Top Hat Transform to extract seamounts. With a combined analysis of basalt dating results from previous studies and gravity anomaly data, an accurate estimation of seamount volume and its spatiotemporal distribution have been obtained. In addition to an asymmetric distribution of the seamounts with a larger volume in the northern flank, clusters of seamounts can be observed at certain locations in the abyssal plain. Due to the consistency between the distribution of the seamount volume and the seafloor spreading features in the South China Sea basin, we propose the ridge jump may induce additional partial melting zones which account for the larger number of seamounts developed in the northern flank while the re-orientation of the extension during seafloor spreading dominated the distribution of post-spreading magmatism. Similar to other marginal basins, magmas formed by spontaneous partial melting would migrate through weak lithosphere where the extension direction changed, resulting in post-spreading magmatism in those lithospheric weak zones.

Robust retinal blood vessel segmentation using hybrid active contour model

In the present scenario, retinal image processing is toiling hard to get an efficient algorithm for de-noising and segmenting the blood vessel confined inside the closed curvature boundary. On this ground, this study presents a hybrid active contour model with a novel preprocessing technique to segment the retinal blood vessel in different fundus images. Contour driven black top-hat transformation and phase-based binarisation method have been implemented to preserve the edge and corner details of the vessels. In the proposed work, gradient vector flow (GVF)-based snake and balloon method are combined to achieve better accuracy over different existing active contour models. In the earlier active contour models, the snake cannot enter inside the closed curvature resulting loss of tiny blood vessels. To circumvent this problem, an inflation term F inf(balloon) with GVF-based snake is incorporated together to achieve the new internal energy of snake for effective vessel segmentation. The evaluation parameters are calculated over four publically available databases: STARE, DRIVE, CHASE, and VAMPIRE. The proposed model outperforms its competitors by calculating a wide range of proven parameters to prove its robustness. The proposed method achieves an accuracy of 0.97 for DRIVE & CHASE and 0.96 for STARE & VAMPIRE datasets.

Detection of hemorrhage in retinal images using linear classifiers and iterative thresholding approaches based on firefly and particle swarm optimization algorithms

We propose a novel iterative thresholding approach based on firefly and particle swarm optimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists of the enhancement of the image using basic preprocessing methods, the segmentation of vessels with the help of Gabor and Top-hat transformation for the removal of the vessels from the image, the determination of the number of regions with hemorrhages and pixel counts in these regions using firefly algorithm (FFA) and particle swarm optimization algorithm (PSOA)-based iterative thresholding, and the detection of hemorrhages with the help of a support vector machine (SVM) and linear regression (LR)-based classifier. In the preprocessing step, color space selection, brightness and contrast adjustment, and adaptive histogram equalization are applied to enhance retinal images, respectively. In the step of segmentation, blood vessels are detected by using Gabor and Top-hat transformations and are removed from the image to avoid confusion with hemorrhagic regions in the retinal image. In the iterative thresholding step, the number of hemorrhagic regions and pixel counts in these regions are determined by using an iterative thresholding approach that generates different thresholding values with the FFA/PSOA. In the classification step, the hemorrhagic regions and pixel counts obtained by the iterative thresholding are used as inputs in the LR/SVM-based classifier. PSOA-based iterative thresholding and the SVM classifier achieved 96.7 % sensitivity, 91.4 % specificity, and 94.1 % accuracy for hemorrhage detection. Finally, the experiments show that the correct classification rates and time performances of the PSOA-based iterative thresholding algorithm are better than those of the FFA in hemorrhage detection. In addition, the proposed approach can be used as a diagnostic decision support system for detecting hemorrhages with high success rate.

A Signal Period Detection Algorithm Based on Morphological Self-Complementary Top-Hat Transform and AMDF

Period detection technology for weak characteristic signals is very important in the fields of speech signal processing, mechanical engineering, etc. Average magnitude difference function (AMDF) is a widely used method to extract the period of periodic signal for its low computational complexity and high accuracy. However, this method has low detection accuracy when the background noise is strong. In order to improve this method, this paper proposes a new method of period detection of the signal with single period based on the morphological self-complementary Top-Hat (STH) transform and AMDF. Firstly, the signal is de-noised by the morphological self-complementary Top-Hat transform. Secondly, the average magnitude difference function of the noise reduction sequence is calculated, and the falling trend is suppressed. Finally, a calculating adaptive threshold is used to extract the peaks at the position equal to the period of periodic signal. The experimental results show that the accuracy of periodic extraction of AMDF after Top-Hat filtering is better than that of AMDF directly. In summary, the proposed method is reliable and stable for detecting the periodic signal with weak characteristics.

Improved Method for Enhancing Dark Images based on CLAHE and Morphological Reconstruction

Abstract The image acquired are often flawed due to various defects from environmental scenarios such as indoor lighting, cloudy weather etc especially at night time. The dark image contains compressed dynamic range that can be enhanced for understanding the detailed information. In this investigative work, an improved method for enhancing extremely dark images is proposed utilizing the concept of illumination reflection model. This improved technique is based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and reconstruction done using morphological processing with Top-hat transformation. The image is perceived in HSV color system and V component is estimated. This intensity (V) component is normalized and the inverse of the intensity component is computed. Then CLAHE algorithm is applied on the negative image. Then multiscale image enhancement is applied to the resultant image. Gamma enhancement is used to adaptively adjust the brightness component of an image. After gamma enhancement, the results of two gamma enhanced images with different gamma values are obtained. Principal Component Analysis extracts the significant information from these images that can be used for image fusion. During the PCA based image fusion, the weight value is adaptively calculated by using morphological Top-hat transformation to improve the quality of the image and illuminate the inconsistent background pixels. The proposed method focuses detail enhancement of extremely dark images by preserving the edges and structures. Experimental validations and results show the better performance of the proposed method than the existing method in terms of qualitative and quantitative measures.

Automatic Seagrass Disturbance Pattern Identification on Sonar Images

Natural and anthropogenic disturbances are causing degradation and loss of seagrass cover, often in the form of bare patches (potholes) and propeller-scaring from vessels. Degradation of seagrass habitat has increased significantly in recent years with losses totaling some 110 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> per year. With seagrass habitat disappearing at historically unprecedented rates, development of new tools for mapping these disturbances is critical to understanding habitat distribution and seagrass abundance. Current methods for mapping seagrass coverage rely on appropriate meteorological conditions (satellite imagery), are high in cost (aerial photography), or lack resolution (in situ point surveys). All of these methods require low turbidity, and none is capable of automatically detecting bare patches (potholes) in seagrass habitat. Sonar-based methods for mapping seagrass can function in high turbidity, and are not affected by meteorological conditions. Here, we present an automatic method for detecting and quantifying potholes in sidescan sonar images collected in a very shallow, highly disturbed seagrass bed. Acoustic studies of shallow seagrass beds (<;2 m) are scarce due to traditional approaches being limited by reduced horizontal swath in these depth ranges. The main challenges associated with these sidescan sonar images are random ambient noise and uneven backscatter intensity across the image. Our method combines adaptive histogram equalization and top-hat mathematical morphology transformation to remove image noises and irregularities. Then, boundaries of potholes are detected using optimum binarization as well as closing and erosion mathematical morphology filters. This method was applied to several sonar images taken from the Lower Laguna Madre in Texas at less than 2-m depth. Experimental results in comparison with ground-truthing demonstrated the effectiveness method by identifying potholes with 97% accuracy.

Enhanced Detection of Characteristic Vibration Signal of Generator Based on Self-Adapted Multi-Scale Top-Hat Transformation

Enhanced Detection of Characteristic Vibration Signal of Generator Based on Self-Adapted Multi-Scale Top-Hat Transformation

An Ultra-Wideband Frequency System for Non-Destructive Root Imaging.

Understanding the root system architecture of plants as they develop is critical for increasing crop yields through plant phenotyping, and ultra-wideband imaging systems have shown potential as a portable, low-cost solution to non-destructive imaging root system architectures. This paper presents the design, implementation, and analysis of an ultra-wideband imaging system for use in imaging potted plant root system architectures. The proposed system is separated into three main subsystems: a Data Acquisition module, a Data Processing module, and an Image Processing and Analysis module. The Data Acquisition module consists of simulated and experimental implementations of a non-contact synthetic aperture radar system to measure ultra-wideband signal reflections from concealed scattering objects in a pot containing soil. The Data Processing module is responsible for interpreting the measured ultra-wideband signals and producing an image using a delay-and-sum beamforming algorithm. The Image Processing and Analysis module is responsible for improving image quality and measuring root depth and average root diameter in an unsupervised manner. The Image Processing and Analysis module uses a modified top-hat transformation alongside quantization methods based on energy distributions in the image to isolate the surface of the imaged root. Altogether, the proposed subsystems are capable of imaging and measuring concealed taproot system architectures with controlled soil conditions; however, the performance of the system is highly dependent on knowledge of the soil conditions. Smaller roots in difficult imaging conditions require future work into understanding and compensating for unwanted noise. Ultimately, this paper sought to provide insight into improving imaging quality of ultra-wideband (UWB) imaging systems for plant root imaging for other works to be followed.

Prediction of Alzheimer's Disease using Brain Images

Alzheimer’s Disease (AD) is a chronic neuro degenerative disease and its detection in early stage is difficult. Experienced doctors always recommend MRI scans as the first step towards diagnosis. Automating AD’s detection process will help fasten the prediction process. A new technique has been proposed to predict the Alzheimer’s disease. The proposed technique segments the image using marker controlled watershed segmentation with top hat transformation and applies the threshold on the image to extract the hippocampus region of the brain. Threshold value has been set for Alzheimer’s Disease using Open Access Series of Imaging Studies (OASIS) database. The efficiency of the proposed work has been shown with the result of the Alzheimer’s disease prediciton using OASIS dataset.

A novel classification scheme to decline the mortality rate among women due to breast tumor.

Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer-aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. The breast carcinomas are classified on the basis of observed classes. This is highly dependent on feature extraction process. In propose work, a novel algorithm for classification based on the combination of top Hat transformation and gray level co-occurrence matrix with back propagation neural network. The aim of this study is to present a robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics. The proposed method is verified on two datasets MIAS and DDSM. It is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.

An Iterative Black Top Hat Transform Algorithm for the Volume Estimation of Lunar Impact Craters

Volume estimation is a fundamental problem in the morphometric study of impact craters. The Top Hat Transform function (TH), a gray-level image processing technique has already been applied to gray-level Digital Elevation Model (DEM) to extract peaks and pits in a nonuniform background. In this study, an updated Black Top Hat Transform function (BTH) was applied to quantify the volume of lunar impact craters on the Moon. We proposed an iterative BTH (IBTH) where the window size and slope factor were linearly increased to extract craters of different sizes, along with a novel application of automatically adjusted threshold to remove noise. Volume was calculated as the sum of the crater depth multiplied by the cell area. When tested against the simulated dataset, IBTH achieved an overall relative accuracy of 95%, in comparison with only 65% for BTH. When applied to the Chang’E DEM and LOLA DEM, IBTH not only minimized the relative error of the total volume estimates, but also revealed the detailed spatial distribution of the crater depth. Therefore, the highly automated IBTH algorithm with few input parameters is ideally suited for estimating the volume of craters on the Moon on a global scale, which is important for understanding the early processes of impact erosion.