scholarly journals Pemilihan Region of Interest Secara Otomatis pada Citra Leukosit

2020 ◽  
Vol 2 (2) ◽  
pp. 76-85
Author(s):  
Wahyu Andi Saputra
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Blob Analysis

Leukimia merupakan penyakit pada manusia yang terjadi karena tubuh memproduksi sel darah putih dalam jumlah yang tidak wajar. Dalam penelitian di bidang IT yang berkaitan dengan sel darah putih, umumnya data yang digunakan sebagai pengujian adalah citra sel darah putih yang diambil dengan bantuan mikroskop. Mula-mula, sel darah putih diletakkan pada preparat, diberi pewarnaan untuk mempertegas warna sel, lalu diambil gambar sel tersebut. Citra yang diambil umumnya masih merupakan citra berukuran besar yang jumlahnya lebih dari satu citra sel darah putih. Padahal umumnya, untuk melakukan ekstraksi fitur, diperlukan satu sel darah putih pada satu citra. Hal ini menjadi pekerjaan tersendiri pada sebuah penelitian. Upaya untuk memilih area sel darah putih dapat dilakukan dengan pendekatan pengolahan citra. Citra yang berukuran besar dan bisa terdiri dari berbagai sel darah putih akan dipilih areanya agar menjadi area yang lebih spesifik. Hal ini disebut dengan Region of Interest. Penelitian ini bertujuan untuk memilih Region of Interest pada sel darah putih secara otomatis dengan menggunakan teknik Blob Analysis yang memanfaatkan BoundingBox. Dengan bantuan citra ground-truth yang didapat dari pakar, area ini kemudian menjadi rujukan dalam menandai koordinat sel darah putih pada citra aslinya. Kemudian, kordinat tersebut diterapkan pada citra asli untuk dilakukan pemotongan agar menjadi citra yang lebih kecil dan memiliki satu sel darah putih. Pengujian dilakukan pada 250 citra berbagai jenis sel darah putih. Dari pengujian, didapatkan hasil Region of Interest dari citra dengan tingkat ketelitian mencapai 99.95%. Hasil penelitian diharapkan dapat memudahkan peneliti dalam mengembangkan peneltian lebih jauh pada citra sel darah putih.

scholarly journals Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

2021 ◽  
Vol 6 (1) ◽  
pp. e000898
Author(s):  
Andrea Peroni ◽  
Anna Paviotti ◽  
Mauro Campigotto ◽  
Luis Abegão Pinto ◽  
Carlo Alberto Cutolo ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Uncertainty Estimation ◽  
Depth Of Field ◽  
Clinical Settings ◽  
Proposed Model ◽  
Validation Experiment ◽  
Segmentation Accuracy ◽  
Ground Truth Image

ObjectiveTo develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysisWe used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.ResultsThe model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.ConclusionThe proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

scholarly journals Stenosis Detection Algorithm for Coronary Angiograms

2019 ◽  
Vol 8 (9) ◽  
pp. 2362-2367
Keyword(s):  
Coronary Artery ◽  
Coronary Vessels ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Vital Role ◽  
Coronary Artery Segment ◽  
Blob Analysis ◽  
Artery Segment ◽  
Stenosis Detection ◽  
Background Image

Automatic detection of blocks in the angiographic images is a challenging task. The features such as contrast and gradient of the vessels and the background image are playing a vital role in the detection of the blocks in the X-Ray angiograms. Nowadays, doctors manually identify blocks in the coronary vessels. The automation tool is necessary to identify the blocks in the blood vessels of the heart to help the doctors in the diagnosing process. Spatiotemporal nature of the angiography sequences is used to isolate the coronary artery tree. The coronary artery segment is tracked and in each image frame by frame and the arterial width surface is detected. The stenosis identification is done by using coronary vessel surface’s persistent minima and blob analysis. The proposed method is experimented on 42 patients’ dataset. The performance of the proposed method was evaluated by comparing the blocks identified by the algorithm with the hand-labelled ground truth images given by the experts. The proposed method provides an accuracy of 95.5% on 42 patients with a total of 60 image runs.

scholarly journals Product Label Reading System for Blind People using Support Vector Machine Algorithm

2019 ◽  
Vol 8 (6S2) ◽  
pp. 179-186
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Region Of Interest ◽  
Ground Truth ◽  
Support Vector ◽  
Svm Classifier ◽  
Stroke Width ◽  
Optical Character ◽  
Character Size ◽  
Reading System

Theoretical—This paper shows a camera based assistive content perusing of item marks from articles to support outwardly tested individuals. Camera fills in as fundamental wellspring of info. To recognize the items, the client will move the article before camera and this moving item will be identified by Background Subtraction (BGS) Method. Content district will be naturally confined as Region of Interest (ROI). Content is extricated from ROI by consolidating both guideline based and learning based technique. A tale standard based content limitation calculation is utilized by recognizing geometric highlights like pixel esteem, shading force, character size and so forth and furthermore highlights like Gradient size, slope width and stroke width are found out utilizing SVM classifier and a model is worked to separate content and non-content area. This framework is coordinated with OCR (Optical Character Recognition) to extricate content and the separated content is given as a voice yield to the client. The framework is assessed utilizing ICDAR-2011 dataset which comprise of 509 common scene pictures with ground truth.

scholarly journals Portrait Quality Assessment using Multi-Scale CNN

2021 ◽  
Vol 2021 (1) ◽  
pp. 5-10
Author(s):  
Chahine Nicolas ◽  
Belkarfa Salim
Keyword(s):  
Neural Network ◽  
Quality Assessment ◽  
Deep Neural Network ◽  
Image Quality Assessment ◽  
Region Of Interest ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Texture Measurement ◽  
Multi Scale ◽  
Lighting Conditions

In this paper, we propose a novel and standardized approach to the problem of camera-quality assessment on portrait scenes. Our goal is to evaluate the capacity of smartphone front cameras to preserve texture details on faces. We introduce a new portrait setup and an automated texture measurement. The setup includes two custom-built lifelike mannequin heads, shot in a controlled lab environment. The automated texture measurement includes a Region-of-interest (ROI) detection and a deep neural network. To this aim, we create a realistic mannequins database, which contains images from different cameras, shot in several lighting conditions. The ground-truth is based on a novel pairwise comparison technology where the scores are generated in terms of Just-Noticeable-differences (JND). In terms of methodology, we propose a Multi-Scale CNN architecture with random crop augmentation, to overcome overfitting and to get a low-level feature extraction. We validate our approach by comparing its performance with several baselines inspired by the Image Quality Assessment (IQA) literature.

scholarly journals Automatic Extraction of Material Defect Size by Infrared Image Sequence

2020 ◽  
Vol 10 (22) ◽  
pp. 8248
Author(s):  
Lihua Yuan ◽  
Xiao Zhu ◽  
Quanbin Sun ◽  
Haibo Liu ◽  
Peter Yuen ◽  
...  
Keyword(s):  
Infrared Image ◽  
Region Of Interest ◽  
Image Sequence ◽  
General Information ◽  
Fiber Reinforced Polymer ◽  
Key Frame ◽  
Reinforced Polymer ◽  
Blob Analysis ◽  
Different Depths ◽  
Infrared Image Sequence

A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame from the sequence is an important problem to be solved first. A maximum standard deviation of the sensitive region method was proposed, which can identify a reasonable image frame automatically from an infrared image sequence; then, a stratagem of image composition was applied for enhancing the detection of deep defects in the key frame. Blob analysis had been adopted to acquire general information of defects such as their distributions and total number of defects. A region of interest of the defect was automatically located by its key frame combined with blob analysis. The defect information was obtained through image segmentation techniques. To obtain a robustness of results, a method of two steps of detection was proposed. The specimen of polyvinyl chloride with two artificial defects at different depths as an example was used to demonstrate how to operate the proposed method for an accurate result. At last, the proposed method was successfully adopted to examine the damage of carbon fiber-reinforced polymer. A comparative study between the proposed method and several state-of-the-art ones shows that the former is accurate and reliable and may provide a more useful and reliable tool for quality assurance in the industrial and manufacturing sectors.

scholarly journals Recognition of overlapping elliptical objects in a binary image

2021 ◽  
Author(s):  
Tong Zou ◽  
Tianyu Pan ◽  
Michael Taylor ◽  
Hal Stern
Keyword(s):  
Synthetic Data ◽  
Region Of Interest ◽  
Ground Truth ◽  
Cell Segmentation ◽  
Adjusted Rand Index ◽  
Bloodstain Pattern Analysis ◽  
Data Set ◽  
Bubble Detection ◽  
Bloodstain Pattern ◽  
Minimal Subset

AbstractRecognition of overlapping objects is required in many applications in the field of computer vision. Examples include cell segmentation, bubble detection and bloodstain pattern analysis. This paper presents a method to identify overlapping objects by approximating them with ellipses. The method is intended to be applied to complex-shaped regions which are believed to be composed of one or more overlapping objects. The method has two primary steps. First, a pool of candidate ellipses are generated by applying the Euclidean distance transform on a compressed image and the pool is filtered by an overlaying method. Second, the concave points on the contour of the region of interest are extracted by polygon approximation to divide the contour into segments. Then, the optimal ellipses are selected from among the candidates by choosing a minimal subset that best fits the identified segments. We propose the use of the adjusted Rand index, commonly applied in clustering, to compare the fitting result with ground truth. Through a set of computational and optimization efficiencies, we are able to apply our approach in complex images comprised of a number of overlapped regions. Experimental results on a synthetic data set, two types of cell images and bloodstain patterns show superior accuracy and flexibility of our method in ellipse recognition, relative to other methods.

scholarly journals Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Qiong Chen ◽  
Yalin Wang ◽  
Xiangyu Liu ◽  
Xi Long ◽  
Bin Yin ◽  
...  
Keyword(s):  
Heart Rate ◽  
Body Movement ◽  
Region Of Interest ◽  
Ground Truth ◽  
Absolute Error ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Majority Voting ◽  
Thermal Cameras ◽  
Electrocardiogram Ecg

Abstract Background Heart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant. Recently, for measuring HR, novel RGB camera-based non-contact techniques have demonstrated their specific superiority compared with other techniques, such as dopplers and thermal cameras. However, they still suffered poor robustness in infants’ HR measurements due to frequent body movement. Methods This paper introduces a framework to improve the robustness of infants’ HR measurements by solving motion artifact problems. Our solution is based on the following steps: morphology-based filtering, region-of-interest (ROI) dividing, Eulerian video magnification and majority voting. In particular, ROI dividing improves ROI information utilization. The majority voting scheme improves the statistical robustness by choosing the HR with the highest probability. Additionally, we determined the dividing parameter that leads to the most accurate HR measurements. In order to examine the performance of the proposed method, we collected 4 hours of videos and recorded the corresponding electrocardiogram (ECG) of 9 hospitalized neonates under two different conditions—rest still and visible movements. Results Experimental results indicate a promising performance: the mean absolute error during rest still and visible movements are 3.39 beats per minute (BPM) and 4.34 BPM, respectively, which improves at least 2.00 and 1.88 BPM compared with previous works. The Bland-Altman plots also show the remarkable consistency of our results and the HR derived from the ground-truth ECG. Conclusions To the best of our knowledge, this is the first study aimed at improving the robustness of neonatal HR measurement under motion artifacts using an RGB camera. The preliminary results have shown the promising prospects of the proposed method, which hopefully reduce neonatal mortality in hospitals.

scholarly journals Development of MATLAB GUI Based on Blob Analysis for Automatic Extraction of Defects

2021 ◽  
Author(s):  
Kang Hong ◽  
Lihua Yuan ◽  
Zhe Li
Keyword(s):  
Time History ◽  
Region Of Interest ◽  
General Information ◽  
Automatic Extraction ◽  
Sequence Diagrams ◽  
Detection Technology ◽  
Quantitative Extraction ◽  
Blob Analysis ◽  
Defect Extraction ◽  
Gray Values

Abstract This study introduces a graphical user interface (GUI) based on MATLAB to realize the automatic ex-traction of sizes of defects from the infrared sequence. To obtain the edge of the defect at deeper layer, a fusion stratagem of the maximum of gray values is adopted for an image subset in the sequence. Blob analysis to the fusion image is used to obtain the general information of defects of a specimen including the distributions and numbers of defects. The frame image is determined for a certain defect according to the peak of the time history curve of sensitive region variance. It can yield a region of interest (ROI) to expand the blob in the selected frame and the defect can be acquired by image segmentation. The results show that through this GUI, a better thermal image can be selected from a set of infrared sequence diagrams for quantitative extraction of different buried depth defect areas, which realizes automatic defect extraction, and its relative error is within 5%. The research on infrared automatic detection technology has certain significance.

Automated histopathological evaluation of pterygium using artificial intelligence

2022 ◽  
pp. bjophthalmol-2021-320141
Author(s):  
Jong Hoon Kim ◽  
Young Jae Kim ◽  
Yeon Jeong Lee ◽  
Joon Young Hyon ◽  
Sang Beom Han ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
External Validation ◽  
Region Of Interest ◽  
Ground Truth ◽  
True Positive Rate ◽  
Automated Method ◽  
Expectation Maximisation ◽  
Histopathological Evaluation ◽  
Histopathological Images ◽  
Automated Grading

PurposeThis study aimed to evaluate the efficacy of a new automated method for the evaluation of histopathological images of pterygium using artificial intelligence.MethodsAn in-house software for automated grading of histopathological images was developed. Histopathological images of pterygium (400 images from 40 patients) were analysed using our newly developed software. Manual grading (I–IV), labelled based on an established scoring system, served as the ground truth for training the four-grade classification models. Region of interest segmentation was performed before the classification of grades, which was achieved by the combination of expectation-maximisation and k-nearest neighbours. Fifty-five radiomic features extracted from each image were analysed with feature selection methods to examine the significant features. Five classifiers were evaluated for their ability to predict quantitative grading.ResultsAmong the classifier models applied for automated grading in this study, the bagging tree showed the best performance, with a 75.9% true positive rate (TPR) and 75.8% positive predictive value (PPV) in internal validation. In external validation, the method also demonstrated reproducibility, with an 81.3% TPR and 82.0% PPV for the average of four classification grades.ConclusionsOur newly developed automated method for quantitative grading of histopathological images of pterygium may be a reliable method for quantitative analysis of histopathological evaluation of pterygium.

scholarly journals A Hybrid Method for Endocardial Contour Extraction of Right Ventricle in 4-Slices from 3D Echocardiography Dataset

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Faten A. Dawood ◽  
Rahmita W. Rahmat ◽  
Suhaini B. Kadiman ◽  
Lili N. Abdullah ◽  
Mohd D. Zamrin
Keyword(s):  
Hybrid Method ◽  
Search Algorithm ◽  
Region Of Interest ◽  
Ground Truth ◽  
Speckle Noise ◽  
Contour Detection ◽  
3D Echocardiography ◽  
Phase Iii ◽  
Contour Extraction ◽  
Endocardial Contour

This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours.

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