Selective Hole Filling of Red Blood Cells for Improved Marker-Controlled Watershed Segmentation

Manual counting and evaluation of red blood cells with the presence of malaria parasites is a tiresome, time-consuming process that can be altered by environmental conditions and human error. Many algorithms were presented to segment red blood cells for subsequent parasitemia evaluation by machine learning algorithms. However, the segmentation of overlapping red blood cells always has been a challenge. Marker-controlled watershed segmentation is one of the methods that was implemented to separate overlapping red blood cells. However, a high number of overlapped red blood cells were still an issue. We propose a novel approach to improve the segmentation efficiency of marker-controlled watershed segmentation. Local minimum histogram background segmentation with a selective hole filling algorithm was introduced to improve segmentation efficiency of marker-controlled watershed segmentation on a high number of overlapping red blood cells. The local minimum was selected on the smoothed histogram for background segmentation. The combination of selective filling, convex hull, and Hough circle detection algorithms was utilized for the intact segmentation of red blood cells. The markers were computed from the resulted mask, and finally, marker-controlled watershed segmentation was applied to separate overlapping red blood cells. As a result, the proposed algorithm achieved higher background segmentation accuracy compared to popular background segmentation algorithms, and the inclusion of corner details improved watershed segmentation efficiency.

Brain Tumor Detection with Biologically Inspired Watershed Segmentation and Classification Based on Feed-Forward Neural Network (FNN)

Image segmentation plays a very vital role in gathering information by dividing the images into various segments to achieve the meaningful information, whereas the image segmentation gives importance in the area of medical imaging to analyze and process the anatomical structures of various internal organs of the body with high resolution images that are captured during medical examination. Medical experts will go through the reports which give the various reasons for the existence of the disease. Brain which is considered the important part of the body so the detection and the segmentation of brain tumors will be considered as the major task of the medical field whereas they are using the high resolution images in the form of MRI reports. The MRI images are considered as the vital source for the identification of tumors in the brain. The accuracy of the segmentation and identification of the tumor depends upon the experience of the radiologist and also it is time consuming task. Therefore the watershed segmentation is performed for the extraction of the tumor region and the features are extracted for the classification, whereas the classification is carried out by the Feed-Forward Neural Network (FNN). The experimental results are evaluated based on the performance and the quality analysis, Furthermore the results give the accuracy of 91.2% in the training model and 71.8% as the testing during the classification process.

Distance transform-watershed segmentation and multi-layer perceptron algorithm to separate touching orange fruit in digital images

An algorithm to separate touching oranges using a distance transform-watershed segmentation is presented. In this study, there are four classes of oranges, such as class A, B, C, and D, respectively. The size of each class is based on the Indonesian National Standard (SNI), the sample used is 168 oranges of which 140 are for training and 28 oranges are for testing. The image of citrus fruits was captured using Kinect v2 camera with a camera resolution of 1920 × 1080 pixels, the distance from the camera to the background is 23 cm. The images were captured in PNG format. The watersheds were computed based on the distance transformed by orange regions. The corresponding basins were finally used to split the falsely connected corn kernel by intersecting the basins with the corn kernel regions. Experimental results show that the multi-layer perceptrons have classification accuracy rates of 92.85%. The algorithm appears to be robust enough to separate most of the multiple touching scenarios.

Diffusion Tensor Imaging Features of Watershed Segmentation Algorithm for Analysis of the Relationship between Depression and Brain Nerve Function of Patients with End-Stage Renal Disease

The aim of this research was to explore the relationship between depression and brain nerve function in patients with end-stage renal disease (ESRD) and long-term maintenance hemodialysis (MHD) based on watershed segmentation algorithm using diffusion tensor imaging (DTI) technology. A total of 29 ESRD patients with depression who received MHD treatment in the hemodialysis center of hospital were included as the research subjects (case group). A total of 29 healthy volunteers were recruited as the control group, and a total of 29 ESRD patients with depression and brain lesions were recruited as the control group (HC group). Within 24 h after hemodialysis, the blood biochemical indexes were collected before this DTI examination. All participants completed the neuropsychological scale (MoCA, TMT A, DST, SAS, and SDS) test. The original DTI data of all subjects were collected and processed based on watershed segmentation algorithm, and the results of automatic segmentation according to the image were evaluated as DSC = 0.9446, MPA = 0.9352, and IOU = 0.8911. Finally, the average value of imaging brain neuropathy in patients with depression in the department of nephrology was obtained. The differences in neuropsychological scale scores (PSQI, MoCA, TMTA, DST, SAS, and SDS) between the two groups were statistically significant ( P < 0.05 ). The differences of FA values in all the white matter partitions of Fu organs, except the cingulum of hippocampus (CgH) between the two groups, were statistically significant ( P < 0.05 ). ESRD and DTI quantitative detection under the guidance of watershed segmentation algorithm in MHD patients showed that ESRD patients can be early identified, so as to carry out psychological nursing as soon as possible to reduce the occurrence of depression, and then protect the brain nerve to reduce brain neuropathy.

Region-Growing Algorithm on CT Angiography Images for Detection of Gynecological Malignant Tumor

This paper aimed to explore pelvic lymphadenectomy for gynecological malignant tumors guided by computed tomography angiography (CTA) images under region-growing algorithm (RGA). 100 cases of malignant tumor patients who received pelvic lymphadenectomy in hospital from January 2018 to January 2020 were analyzed. Patients were classified into control group (CTA image) and experimental group (RGA-based CTA image), each with 50 cases. The overall accuracy (OA) of the pelvic CT image segmentation parameters under RGA, the watershed segmentation algorithm (WA), and the swarm intelligence optimization algorithm (SIOA) was compared. Comparisons of segmentation parameters, denoising performance, and CT imaging of patients as well as diagnosis rate and total efficiency rate were carried out. The results showed that overall accuracy (OA) of RGA was considerably higher versus watershed segmentation algorithm (WA) and swarm intelligence optimization algorithm (SIOA). However, false positive rate (FPR) and false negative rate (FNR) of RGA were greatly lower than those of other algorithms. RGA greatly improved the accuracy of pelvic tumor detection. The peak signal-to-noise ratio (PSNR) of RGA was superior to that of WA and SIOA, but differences in edge preservation index (EPI) value were not significant. The diagnosis rate of the experimental group was 48/50 (96%), while the diagnosis rate by manual means was 38/50 (76%). For the diagnosis rate and total efficiency, results of the experimental group were evidently higher in contrast to the control group ( P < 0.05 ). In conclusion, under RGA, CTA image-guided pelvic lymphadenectomy had good segmentation accuracy and denoising performance, and it was superior in total efficiency and diagnosis rate, which was worthy of clinical promotion.

Using marker-controlled watershed transform to detect baker’s cyst in MRI images: a pilot study

Introduction: Nowadays, Magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee’s cyst (especially baker) segmentation is one of the novel research areas.Material and Method: There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker-controlled watershed segmentation for the detection of baker’s cyst in the knee’s joint MRI sagittal and axial T2-weighted images.Results: The performance of this algorithm was investigated, and the results showed that in a short time baker’s cyst can be clearly extracted from original images in axial and sagittal planes.Conclusion: The marker-controlled watershed segmentation was able to detect baker’s cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MR images.

Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs

Fine-scale sea ice conditions are key to our efforts to understand and model climate change. We propose the first deep learning pipeline to extract fine-scale sea ice layers from high-resolution satellite imagery (Worldview-3). Extracting sea ice from imagery is often challenging due to the potentially complex texture from older ice floes (i.e., floating chunks of sea ice) and surrounding slush ice, making ice floes less distinctive from the surrounding water. We propose a pipeline using a U-Net variant with a Resnet encoder to retrieve ice floe pixel masks from very-high-resolution multispectral satellite imagery. Even with a modest-sized hand-labeled training set and the most basic hyperparameter choices, our CNN-based approach attains an out-of-sample F1 score of 0.698–a nearly 60% improvement when compared to a watershed segmentation baseline. We then supplement our training set with a much larger sample of images weak-labeled by a watershed segmentation algorithm. To ensure watershed derived pack-ice masks were a good representation of the underlying images, we created a synthetic version for each weak-labeled image, where areas outside the mask are replaced by open water scenery. Adding our synthetic image dataset, obtained at minimal effort when compared with hand-labeling, further improves the out-of-sample F1 score to 0.734. Finally, we use an ensemble of four test metrics and evaluated after mosaicing outputs for entire scenes to mimic production setting during model selection, reaching an out-of-sample F1 score of 0.753. Our fully-automated pipeline is capable of detecting, monitoring, and segmenting ice floes at a very fine level of detail, and provides a roadmap for other use-cases where partial results can be obtained with threshold-based methods but a context-robust segmentation pipeline is desired.