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Automated localization and segmentation techniques for B-mode ultrasound images: A review

Meiburger KM, Acharya UR, Molinari F

Comput Biol Med, 2018 01 01;92:210-235.
PMID: 29247890 DOI: 10.1016/j.compbiomed.2017.11.018

B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed.

Matched MeSH terms: Image Interpretation, Computer-Assisted*
FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor classification

Hossain A, Islam R, Islam MT, Kirawanich P, Soliman MS

Comput Biol Med, 2024 Dec;183:109316.
PMID: 39489108 DOI: 10.1016/j.compbiomed.2024.109316

The microwave brain imaging (MBI) system is an emerging technology used to detect brain tumors in their early stages. Multi-class microwave-based brain tumor (MBT) identification and classification are crucial due to the tumor's patterns and shape. Manual identification and categorization of the tumors from the images by physicians is a challenging task and consumes more time. Recently, to overcome these issues, the deep transfer learning (DTL) technique has been used to classify brain tumors efficiently. This paper proposes a Fine-tuned Feature Extracted Deep Transfer Learning Model called FT-FEDTL for multi-class MBT classification purposes. The main objective of this work is to suggest a better pathway for brain tumor diagnosis by designing an efficient DTL model that automatically identifies and categorizes the MBT images. The InceptionV3 architecture is utilized as a base for feature extraction in the proposed FT-FEDTL model. Thereafter, a fine-tuning method is applied to the additional five layers with hyperparameters. The fine-tuned layers are attached to the base model to enhance classification performance. The MBT data are collected from two sources and balanced by augmentation techniques to create a total of 4200 balanced datasets. Later, 80 % images are used for training, 20 % images are utilized for validation, and 80 samples of each class are used for testing the FT-FEDTL model for classifying tumors into six classes. We evaluated and compared the FT-FEDTL model with the three traditional non-CNN and seven pretrained models by applying an imbalanced and balanced dataset. The proposed model showed superior classification performance compared to other models for the balanced dataset. It attained an overall accuracy, recall, precision, specificity, and Fscore of 99.65 %, 99.16 %, 99.48 %, 99.10 %, and 99.23 %, respectively. The experimental outcomes ensure that the proposed model can be employed in biomedical applications to assist radiologists for multi-class MBT image classification purposes. The Anaconda distribution platform with Python 3.7 on the Windows 11 OS is used to implement the models.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
Use of image analysis in determining masticatory efficiency in patients presenting for immediate dentures

Mahmood WA, Watson CJ, Ogden AR, Hawkins RV

Int J Prosthodont, 1992 Jul-Aug;5(4):359-66.
PMID: 1520458

Image analysis was used to determine masticatory efficiency and performance before and after placement of immediate dentures. Sections of cored carrot were used as the test food and the particle size of chewed expectorated food was measured using image analysis. Measurements were shown to be accurate and reproducible. Masticatory function of immediate-denture patients was also compared with a similar number of dentate individuals and experienced complete-denture wearers. Dentate subjects were significantly (P less than .01) more efficient at masticating the test food than were the complete- or immediate-denture wearers. The new method of measurement removes the necessity for the unpleasant and unhygienic sieving process previously used in this type of study.

Matched MeSH terms: Image Interpretation, Computer-Assisted*
Speckle Noise Diffusion in Knee Articular Cartilage Ultrasound Images

Shoaib MA, Hossain MB, Hum YC, Chuah JH, Mohd Salim MI, Lai KW

Curr Med Imaging, 2020;16(6):739-751.
PMID: 32723246 DOI: 10.2174/1573405615666190903143330

BACKGROUND: Ultrasound (US) imaging can be a convenient and reliable substitute for magnetic resonance imaging in the investigation or screening of articular cartilage injury. However, US images suffer from two main impediments, i.e., low contrast ratio and presence of speckle noise.
AIMS: A variation of anisotropic diffusion is proposed that can reduce speckle noise without compromising the image quality of the edges and other important details.
METHODS: For this technique, four gradient thresholds were adopted instead of one. A new diffusivity function that preserves the edge of the resultant image is also proposed. To automatically terminate the iterative procedures, the Mean Absolute Error as its stopping criterion was implemented.
RESULTS: Numerical results obtained by simulations unanimously indicate that the proposed method outperforms conventional speckle reduction techniques. Nevertheless, this preliminary study has been conducted based on a small number of asymptomatic subjects.
CONCLUSION: Future work must investigate the feasibility of this method in a large cohort and its clinical validity through testing subjects with a symptomatic cartilage injury.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Automated approach for detection of ischemic stroke using Delaunay Triangulation in brain MRI images

Subudhi A, Acharya UR, Dash M, Jena S, Sabut S

Comput Biol Med, 2018 12 01;103:116-129.
PMID: 30359807 DOI: 10.1016/j.compbiomed.2018.10.016

It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic resonance imaging (MRI) images due to variation in different lesion sizes, variation in morphological structure, and similarity in intensity of lesion with normal brain in three types of stroke, namely partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). In this paper, we have integrated the advantages of Delaunay triangulation (DT) and fractional order Darwinian particle swarm optimization (FODPSO), called DT-FODPSO technique for automatic segmentation of the structure of the stroke lesion. The approach was validated on 192 MRI images obtained from different stroke subjects. Statistical and morphological features were extracted and classified according to the Oxfordshire community stroke project (OCSP) using support vector machine (SVM) and random forest (RF) classifiers. The method effectively detected the stroke lesions and achieved promising results with an average sensitivity of 0.93, accuracy of 0.95, JI of 0.89 and Dice similarity index of 0.93 using RF classifier. These promising results indicates the DT based optimized approach is efficient in detecting ischemic stroke and it can aid the neuro-radiologists to validate their routine screening.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques

Koh JEW, Ng EYK, Bhandary SV, Hagiwara Y, Laude A, Acharya UR

Comput Biol Med, 2018 01 01;92:204-209.
PMID: 29227822 DOI: 10.1016/j.compbiomed.2017.11.019

Untreated age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma may lead to irreversible vision loss. Hence, it is essential to have regular eye screening to detect these eye diseases at an early stage and to offer treatment where appropriate. One of the simplest, non-invasive and cost-effective techniques to screen the eyes is by using fundus photo imaging. But, the manual evaluation of fundus images is tedious and challenging. Further, the diagnosis made by ophthalmologists may be subjective. Therefore, an objective and novel algorithm using the pyramid histogram of visual words (PHOW) and Fisher vectors is proposed for the classification of fundus images into their respective eye conditions (normal, AMD, DR, and glaucoma). The proposed algorithm extracts features which are represented as words. These features are built and encoded into a Fisher vector for classification using random forest classifier. This proposed algorithm is validated with both blindfold and ten-fold cross-validation techniques. An accuracy of 90.06% is achieved with the blindfold method, and highest accuracy of 96.79% is obtained with ten-fold cross-validation. The highest classification performance of our system shows the potential of deploying it in polyclinics to assist healthcare professionals in their initial diagnosis of the eye. Our developed system can reduce the workload of ophthalmologists significantly.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
CT Reconstruction Algorithm and Low Contrast Detectability of Phantom Study: A Systematic Review and Meta-Analysis

Adibah Yusof NA, Abdul Karim MK, Asikin NM, Paiman S, Awang Kechik MM, Abdul Rahman MA, et al.

Curr Med Imaging, 2023;19(10):1105-1113.
PMID: 35975862 DOI: 10.2174/1573405618666220816160544

BACKGROUND: For almost three decades, computed tomography (CT) has been extensively used in medical diagnosis, which led researchers to conduct linking of CT dose exposure with image quality.
METHODS: In this study, a systematic review and a meta-analysis study were conducted on CT phantom for resolution study especially based on the low contrast detectability (LCD). Furthermore, the association between the CT parameter such as tube voltage and the type of reconstruction algorithm, the amount of phantom scanning affecting the image quality and the exposure dose were also investigated in this study. We utilize PubMed, ScienceDirect, Google Scholar and Scopus databases to search related published articles from the year 2011 until 2020. The notable keywords comprise "computed tomography", "CT phantom", and "low contrast detectability". Of 52 articles, 20 articles are within the inclusion criteria in this systematic review.
RESULTS: The dichotomous outcomes were chosen to represent the results in terms of risk ratio as per meta-analysis study. Notably, the noise in iterative reconstruction (IR) reduced by 24%, 33% and 36% with the use of smooth, medium and sharp filters, respectively. Furthermore, adaptive iterative dose reduction (AIDR 3D) improved image quality and the visibility of smaller less dense objects compared to filtered back-projection. Most of the researchers used 120 kVp tube voltage to scan phantom for quality assurance study.
CONCLUSION: Hence, optimizing primary factors such as tube potential reduces the dose exposure significantly, and the optimized IR technique could substantially reduce the radiation dose while maintaining the image quality.

Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods
ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images

Kaplan E, Baygin M, Barua PD, Dogan S, Tuncer T, Altunisik E, et al.

Med Eng Phys, 2023 May;115:103971.
PMID: 37120169 DOI: 10.1016/j.medengphy.2023.103971

PURPOSE: The classification of medical images is an important priority for clinical research and helps to improve the diagnosis of various disorders. This work aims to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy.
MATERIALS AND METHOD: This work uses two (private and public) datasets. The private dataset consists of 3807 magnetic resonance imaging (MRI) and computer tomography (CT) images belonging to two (normal and AD) classes. The second public (Kaggle AD) dataset contains 6400 MR images. The presented classification model comprises three fundamental phases: feature extraction using an exemplar hybrid feature extractor, neighborhood component analysis-based feature selection, and classification utilizing eight different classifiers. The novelty of this model is feature extraction. Vision transformers inspire this phase, and hence 16 exemplars are generated. Histogram-oriented gradients (HOG), local binary pattern (LBP) and local phase quantization (LPQ) feature extraction functions have been applied to each exemplar/patch and raw brain image. Finally, the created features are merged, and the best features are selected using neighborhood component analysis (NCA). These features are fed to eight classifiers to obtain highest classification performance using our proposed method. The presented image classification model uses exemplar histogram-based features; hence, it is called ExHiF.
RESULTS: We have developed the ExHiF model with a ten-fold cross-validation strategy using two (private and public) datasets with shallow classifiers. We have obtained 100% classification accuracy using cubic support vector machine (CSVM) and fine k nearest neighbor (FkNN) classifiers for both datasets.
CONCLUSIONS: Our developed model is ready to be validated with more datasets and has the potential to be employed in mental hospitals to assist neurologists in confirming their manual screening of AD using MRI/CT images.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
Fulltext Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models

Tan SL, Selvachandran G, Ding W, Paramesran R, Kotecha K

Interdiscip Sci, 2024 Mar;16(1):16-38.
PMID: 37962777 DOI: 10.1007/s12539-023-00589-5

As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
Editorial for "Quantitative Analysis of Whole-Body MRI for Accessing the Degree of Diffuse Infiltration Patterns and Identifying High Risk Cases of Newly Diagnosed Multiple Myeloma"

Azlan CA, Md Shah MN

J Magn Reson Imaging, 2024 Jun;59(6):2046-2047.
PMID: 37655837 DOI: 10.1002/jmri.28990
Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
Multifocus watermarking approach based on discrete cosine transform

Waheed SR, Alkawaz MH, Rehman A, Almazyad AS, Saba T

Microsc Res Tech, 2016 May;79(5):431-7.
PMID: 26918523 DOI: 10.1002/jemt.22646

Image fusion process consolidates data and information from various images of same sight into a solitary image. Each of the source images might speak to a fractional perspective of the scene, and contains both "pertinent" and "immaterial" information. In this study, a new image fusion method is proposed utilizing the Discrete Cosine Transform (DCT) to join the source image into a solitary minimized image containing more exact depiction of the sight than any of the individual source images. In addition, the fused image comes out with most ideal quality image without bending appearance or loss of data. DCT algorithm is considered efficient in image fusion. The proposed scheme is performed in five steps: (1) RGB colour image (input image) is split into three channels R, G, and B for source images. (2) DCT algorithm is applied to each channel (R, G, and B). (3) The variance values are computed for the corresponding 8 × 8 blocks of each channel. (4) Each block of R of source images is compared with each other based on the variance value and then the block with maximum variance value is selected to be the block in the new image. This process is repeated for all channels of source images. (5) Inverse discrete cosine transform is applied on each fused channel to convert coefficient values to pixel values, and then combined all the channels to generate the fused image. The proposed technique can potentially solve the problem of unwanted side effects such as blurring or blocking artifacts by reducing the quality of the subsequent image in image fusion process. The proposed approach is evaluated using three measurement units: the average of Q(abf) , standard deviation, and peak Signal Noise Rate. The experimental results of this proposed technique have shown good results as compared with older techniques. Microsc. Res. Tech. 79:431-437, 2016. © 2016 Wiley Periodicals, Inc.

Matched MeSH terms: Image Interpretation, Computer-Assisted
Fulltext Distinctive accuracy measurement of binary descriptors in mobile augmented reality

Tan SY, Arshad H, Abdullah A

PLoS One, 2019;14(1):e0207191.
PMID: 30605474 DOI: 10.1371/journal.pone.0207191

Mobile Augmented Reality (MAR) requires a descriptor that is robust to changes in viewing conditions in real time application. Many different descriptors had been proposed in the literature for example floating-point descriptors (SIFT and SURF) and binary descriptors (BRIEF, ORB, BRISK and FREAK). According to literature, floating-point descriptors are not suitable for real-time application because its operating speed does not satisfy real-time constraints. Binary descriptors have been developed with compact sizes and lower computation requirements. However, it is unclear which binary descriptors are more appropriate for MAR. Hence, a distinctive and efficient accuracy measurement of four state-of-the-art binary descriptors, namely, BRIEF, ORB, BRISK and FREAK were performed using the Mikolajczyk dataset and ALOI dataset to identify the most appropriate descriptor for MAR in terms of computation time and robustness to brightness, scale and rotation changes. The obtained results showed that FREAK is the most appropriate descriptor for MAR application as it able to produce an application that are efficient (shortest computation time) and robust towards scale, rotation and brightness changes.

Matched MeSH terms: Image Interpretation, Computer-Assisted
Hardware Implementation of Multiple Fan Beam Projection Technique in Optical Fibre Process Tomography

Rahim RA, Rahiman MH, Chen LL, San CK, Fea PJ

Sensors (Basel), 2008 May 23;8(5):3406-3428.
PMID: 27879885

The main objective of this project is to implement the multiple fan beam projection technique using optical fibre sensors with the aim to achieve a high data acquisition rate. Multiple fan beam projection technique here is defined as allowing more than one emitter to transmit light at the same time using the switch-mode fan beam method. For the thirty-two pairs of sensors used, the 2-projection technique and 4- projection technique are being investigated. Sixteen sets of projections will complete one frame of light emission for the 2-projection technique while eight sets of projection will complete one frame of light emission for the 4-projection technique. In order to facilitate data acquisition process, PIC microcontroller and the sample and hold circuit are being used. This paper summarizes the hardware configuration and design for this project.

Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted
Improvement to the scanning electron microscope image adaptive Canny optimization colorization by pseudo-mapping

Lo TY, Sim KS, Tso CP, Nia ME

Scanning, 2014 Sep-Oct;36(5):530-9.
PMID: 25139061 DOI: 10.1002/sca.21152

An improvement to the previously proposed adaptive Canny optimization technique for scanning electron microscope image colorization is reported. The additional feature, called pseudo-mapping technique, is that the grayscale markings are temporarily mapped to a set of pre-defined pseudo-color map as a mean to instill color information for grayscale colors in chrominance channels. This allows the presence of grayscale markings to be identified; hence optimization colorization of grayscale colors is made possible. This additional feature enhances the flexibility of scanning electron microscope image colorization by providing wider range of possible color enhancement. Furthermore, the nature of this technique also allows users to adjust the luminance intensities of selected region from the original image within certain extent.

Matched MeSH terms: Image Interpretation, Computer-Assisted
A Heuristic Automatic and Robust ROI Detection Method for Medical Image Warermarking

Mousavi SM, Naghsh A, Abu-Bakar SA

J Digit Imaging, 2015 Aug;28(4):417-27.
PMID: 25736857 DOI: 10.1007/s10278-015-9770-z

This paper presents an automatic region of interest (ROI) segmentation method for application of watermarking in medical images. The advantage of using this scheme is that the proposed method is robust against different attacks such as median, Wiener, Gaussian, and sharpening filters. In other words, this technique can produce the same result for the ROI before and after these attacks. The proposed algorithm consists of three main parts; suggesting an automatic ROI detection system, evaluating the robustness of the proposed system against numerous attacks, and finally recommending an enhancement part to increase the strength of the composed system against different attacks. Results obtained from the proposed method demonstrated the promising performance of the method.

Matched MeSH terms: Image Interpretation, Computer-Assisted*
Combined Spline and B-spline for an improved automatic skin lesion segmentation in dermoscopic images using optimal color channel

Abbas AA, Guo X, Tan WH, Jalab HA

J Med Syst, 2014 Aug;38(8):80.
PMID: 24957396 DOI: 10.1007/s10916-014-0080-7

In a computerized image analysis environment, the irregularity of a lesion border has been used to differentiate between malignant melanoma and other pigmented skin lesions. The accuracy of the automated lesion border detection is a significant step towards accurate classification at a later stage. In this paper, we propose the use of a combined Spline and B-spline in order to enhance the quality of dermoscopic images before segmentation. In this paper, morphological operations and median filter were used first to remove noise from the original image during pre-processing. Then we proceeded to adjust image RGB values to the optimal color channel (green channel). The combined Spline and B-spline method was subsequently adopted to enhance the image before segmentation. The lesion segmentation was completed based on threshold value empirically obtained using the optimal color channel. Finally, morphological operations were utilized to merge the smaller regions with the main lesion region. Improvement on the average segmentation accuracy was observed in the experimental results conducted on 70 dermoscopic images. The average accuracy of segmentation achieved in this paper was 97.21 % (where, the average sensitivity and specificity were 94 % and 98.05 % respectively).

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Fulltext A new human heart vessel identification, segmentation and 3D reconstruction mechanism

Al-Surmi A, Wirza R, Mahmod R, Khalid F, Dimon MZ

J Cardiothorac Surg, 2014;9:161.
PMID: 25274253 DOI: 10.1186/s13019-014-0161-1

The identification and segmentation of inhomogeneous image regions is one of the most challenging issues nowadays. The surface vessels of the human heart are important for the surgeons to locate the region where to perform the surgery and to avoid surgical injuries. In addition, such identification, segmentation, and visualisation helps novice surgeons in the training phase of cardiac surgery.

Matched MeSH terms: Image Interpretation, Computer-Assisted*
Fulltext A deformable generic 3D model of haptoral anchor of Monogenean

Teo BG, Dhillon SK, Lim LH

PLoS One, 2013;8(10):e77650.
PMID: 24204903 DOI: 10.1371/journal.pone.0077650

In this paper, a digital 3D model which allows for visualisation in three dimensions and interactive manipulation is explored as a tool to help us understand the structural morphology and elucidate the functions of morphological structures of fragile microorganisms which defy live studies. We developed a deformable generic 3D model of haptoral anchor of dactylogyridean monogeneans that can subsequently be deformed into different desired anchor shapes by using direct manipulation deformation technique. We used point primitives to construct the rectangular building blocks to develop our deformable 3D model. Point primitives are manually marked on a 2D illustration of an anchor on a Cartesian graph paper and a set of Cartesian coordinates for each point primitive is manually extracted from the graph paper. A Python script is then written in Blender to construct 3D rectangular building blocks based on the Cartesian coordinates. The rectangular building blocks are stacked on top or by the side of each other following their respective Cartesian coordinates of point primitive. More point primitives are added at the sites in the 3D model where more structural variations are likely to occur, in order to generate complex anchor structures. We used Catmull-Clark subdivision surface modifier to smoothen the surface and edge of the generic 3D model to obtain a smoother and more natural 3D shape and antialiasing option to reduce the jagged edges of the 3D model. This deformable generic 3D model can be deformed into different desired 3D anchor shapes through direct manipulation deformation technique by aligning the vertices (pilot points) of the newly developed deformable generic 3D model onto the 2D illustrations of the desired shapes and moving the vertices until the desire 3D shapes are formed. In this generic 3D model all the vertices present are deployed for displacement during deformation.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Concentration measurements of bubbles in a water column using an optical tomography system

Ibrahim S, Yunus MA, Green RG, Dutton K

ISA Trans, 2012 Nov;51(6):821-6.
PMID: 22624831 DOI: 10.1016/j.isatra.2012.04.010

Optical tomography provides a means for the determination of the spatial distribution of materials with different optical density in a volume by non-intrusive means. This paper presents results of concentration measurements of gas bubbles in a water column using an optical tomography system. A hydraulic flow rig is used to generate vertical air-water two-phase flows with controllable bubble flow rate. Two approaches are investigated. The first aims to obtain an average gas concentration at the measurement section, the second aims to obtain a gas distribution profile by using tomographic imaging. A hybrid back-projection algorithm is used to calculate concentration profiles from measured sensor values to provide a tomographic image of the measurement cross-section. The algorithm combines the characteristic of an optical sensor as a hard field sensor and the linear back projection algorithm.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
On the use of collinear and triangle equation for automatic segmentation and boundary detection of cardiac cavity images

Sigit R, Mustafa MM, Hussain A, Maskon O, Nor IF

Adv Exp Med Biol, 2011;696:481-8.
PMID: 21431588 DOI: 10.1007/978-1-4419-7046-6_48

In this chapter, the computational biology of cardiac cavity images is proposed. The method uses collinear and triangle equation algorithms to detect and reconstruct the boundary of the cardiac cavity. The first step involves high boost filter to enhance the high frequency component without affecting the low frequency component. Second, the morphological and thresholding operators are applied to the image to eliminate noise and convert the image into a binary image. Next, the edge detection is performed using the negative Laplacian filter and followed by region filtering. Finally, the collinear and triangle equations are used to detect and reconstruct the more precise cavity boundary. Results obtained have proved that this technique is able to perform better segmentation and detection of the boundary of cardiac cavity from echocardiographic images.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods

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