A New Hybrid Order Approach to Morphological Color Image Processing Based on Reduced Order with Adaptive Absolute Reference

Mathematical morphology can process the binary and grayscale image successfully. This theory cannot be extended to the color image directly. In color space, a vector represents a pixel, so in order to compare vectors, vectoriel orderings must be defined first. This paper addresses the question of the extension of morphological operator to the case of color images. The proposed method used the order by bit mixing to replace the conditional order. Our order is based on a combination of reduced and bit mixing ordering of the underlying data. Additionally it is a total ordering. Since it not only solves the problems of false color generated by the marginal order but also those of multiple extrema generated by reduced order. The performance of the introduced operators is illustrated by means of different applications: color gradients for segmenting, image smoothing (noise suppression) by median filter operator and Laplacian operators. Examples of natural color images and synthetic color images are given. Experimental results show the improvement brought by this new method.

Automatic Extraction of Urban Road Centerlines from High-Resolution Satellite Imagery Using Automatic Thresholding and Morphological Operation Method

The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.

Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction.Precise segmentation of brain hemorrhage is crucial,so an enhanced segmentation is carried out in this research work.The brain image of various patients has taken using an MRI scanner by the utilization of T1,T2,and FLAIR sequence.This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet(multires UNet)through morphological operations.It is hard to precisely segment the brain lesions to extract the existing region of stroke.This crucial step is accomplished by this proposed MMU-Net methodology by precise segmentation of stroke lesions.The proposed method efficiently determines the hemorrhagic stroke with improved accuracy of 95%compared with the existing segmentation techniques such as U-net++,ResNet,Multires UNET and 3D-ResU-Net and also provides improved performance of 2D and 3D U-Net with an enhanced outcome.The performancemeasure of the proposed methodology acquires an improved accuracy,precision ratio,sensitivity,and specificity rate of 0.07%,0.04%,0.04%,and 0.05%in comparison to U-net,ResNet,Multires UNET and 3D-ResU-Net techniques respectively.

Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

In recent days,detecting Optic Disc(OD)in retinal images has been challenging and very important to the early diagnosis of eye diseases.The process of detecting the OD is challenging due to the diversity of color,intensity,brightness and shape of the OD.Moreover,the color similarities of the neighboring organs of the OD create difficulties during OD detection.In the proposed Fuzzy K‒Means Threshold(FKMT)and Morphological Operation with Pixel Density Feature(MOPDF),the input retinal images are coarsely segmented by fuzzy K‒means clustering and thresholding,in which the OD is classified from its neighboring organs with intensity similarities.Then,the segmented images are given as the input to morphological operation with pixel density feature calculations,which reduce the false detection in the small pixel of the OD.Finally,the OD area is detected by applying the Sobel edge detection method,which accurately detects the OD from the retinal images.After detection optimization,the proposed method achieved Sensitivity(SEN),Specificity(SPEC)and Accuracy(ACC),with 96.74%,96.78%and 96.92%in DiaretDB0(Standard Diabetic Retinopathy Database Calibration level 0),97.12%,97.10%and 97.75%in DiaretDB1(Standard Diabetic Retinopathy Database Calibration level 1)and 97.19%,97.47%and 97.43%in STARE(Structured Analysis of the Retina)dataset respectively.The experimental results demonstrated the proposed method’s efficiency for segmenting and detecting OD areas.

A Novel Approach for Brain Tumor Detection Using MRI Images

Processing magnetic resonance images are very complex and constantly studied by the researchers to give doctors better ability to diagnose the patients. In order to detect automatically suspicious regions or tumors, we present a new approach inspired by threshold segmentation and based on morphological operations in this paper. The advantages of our approach come from the complementarities between these two approaches. The morphological operations extract roughly the tumor region and eventually can affect healthy while the threshold segmentation method gives a clear picture of the structure of the different brain and therefore these two approaches improve significantly the threshold segmentation and detection and extraction of the tumor zone based on morphological operations.

Efficient Segmentation Approach for Different Medical Image Modalities

This paper presents a study of the segmentation of medical images.The paper provides a solid introduction to image enhancement along with image segmentation fundamentals.In the first step,the morphological operations are employed to ensure image detail protection and noise-immunity.The objective of using morphological operations is to remove the defects in the texture of the image.Secondly,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers.The proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster centers.It is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition matrix.Simulation results are performed on different medical image modalities.Ultrasonic(Us),X-ray(Mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and Magnetic Resonance(MR)images are the main medical image modalities used in this work.The obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image segmentation.Simulation results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PET,and MRI images,respectively.

Hybrid Segmentation Approach for Different Medical Image Modalities

The segmentation process requires separating the image region into sub-regions of similar properties.Each sub-region has a group of pixels having the same characteristics,such as texture or intensity.This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)algorithms.An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques.The existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed algorithms.This paper extensively analyzes and summarizes the comparative investigation of these techniques.Finally,a prediction of the improvement involves the combination of these techniques is suggested.The obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.

Automatic Generation of Water Masks from RapidEye Images

Water is a very important natural resource and it supports all life forms on earth. It is used by humans in various ways including drinking, agriculture and for scientific research. The aim of this research was to develop a routine to automatically extract water masks from RapidEye images, which could be used for further investigation such as water quality monitoring and change detection. A Python-based algorithm was therefore developed for this particular purpose. The developed routine combines three spectral indices namely Simple Ratios (SRs), Normalized Green Index (NGI) and Normalized Difference Water Index (NDWI). The two SRs are calculated between the NIR and green band, and between the NIR and red band. The NGI is calculated by rationing the green band to the sum of all bands in each image. The NDWI is calculated by differencing the green to the NIR and dividing by the sum of the green and NIR bands. The routine generates five intermediate water masks, which are spatially intersected to create a single intermediate water mask. In order to remove very small waterbodies and any remaining gaps in the intermediate water mask, morphological opening and closing were performed to generate the final water mask. This proposed algorithm was used to extract water masks from some RapidEye images. It yielded an Overall Accuracy of 95% and a mean Kappa Statistic of 0.889 using the confusion matrix approach.

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.