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Fusion of gamma scintigraphic and magnetic resonance images improves the anatomical delineation of radiotracer for the assessment of gastrointestinal transit

Yeong CH, Abdullah BJ, Ng KH, Chung LY, Goh KL, Perkins AC

Nucl Med Commun, 2013 Jul;34(7):645-51.
PMID: 23612704 DOI: 10.1097/MNM.0b013e32836141e4

This paper describes the use of gamma scintigraphic and magnetic resonance (MR) fusion images for improving the anatomical delineation of orally administered radiotracers used in gastrointestinal (GI) transit investigations.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches

Abu A, Leow LK, Ramli R, Omar H

BMC Bioinformatics, 2016 Dec 22;17(Suppl 19):505.
PMID: 28155645 DOI: 10.1186/s12859-016-1362-5

BACKGROUND: Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs.
RESULTS: At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community.
CONCLUSIONS: This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Determination of foveal avascular zone in diabetic retinopathy digital fundus images

Ahmad Fadzil MH, Izhar LI, Nugroho HA

Comput Biol Med, 2010 Jul;40(7):657-64.
PMID: 20573343 DOI: 10.1016/j.compbiomed.2010.05.004

Monitoring FAZ area enlargement enables physicians to monitor progression of the DR. At present, it is difficult to discern the FAZ area and to measure its enlargement in an objective manner using digital fundus images. A semi-automated approach for determination of FAZ using color images has been developed. Here, a binary map of retinal blood vessels is computer generated from the digital fundus image to determine vessel ends and pathologies surrounding FAZ for area analysis. The proposed method is found to achieve accuracies from 66.67% to 98.69% compared to accuracies of 18.13-95.07% obtained by manual segmentation of FAZ regions from digital fundus images.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Independent component analysis for assessing therapeutic response in vitiligo skin disorder

Fadzil MH, Norashikin S, Suraiya HH, Nugroho H

J Med Eng Technol, 2009;33(2):101-9.
PMID: 19205989 DOI: 10.1080/03091900802454459

This paper describes an image analysis technique that objectively measures skin repigmentation for the assessment of therapeutic response in vitiligo treatments. Skin pigment disorders due to the abnormality of melanin production, such as vitiligo, cause irregular pale patches of skin. The therapeutic response to treatment is repigmentation of the skin. However the repigmentation process is very slow and is only observable after a few months of treatment. Currently, there is no objective method to assess the therapeutic response of skin pigment disorder treatment, particularly for vitiligo treatment. In this work, we apply principal component analysis followed by independent component analysis to represent digital skin images in terms of melanin and haemoglobin composition respectively. Vitiligo skin areas are identified as skin areas that lack melanin (non-melanin areas). Results obtained using the technique have been verified by dermatologists. Based on 20 patients, the proposed technique effectively monitored the progression of repigmentation over a shorter time period of six weeks and can thus be used to evaluate treatment efficacy objectively and more effectively.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review

Sudarshan VK, Mookiah MR, Acharya UR, Chandran V, Molinari F, Fujita H, et al.

Comput Biol Med, 2016 Feb 1;69:97-111.
PMID: 26761591 DOI: 10.1016/j.compbiomed.2015.12.006

Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers

Ninomiya K, Arimura H, Chan WY, Tanaka K, Mizuno S, Muhammad Gowdh NF, et al.

PLoS One, 2021;16(1):e0244354.
PMID: 33428651 DOI: 10.1371/journal.pone.0244354

OBJECTIVES: To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs).
MATERIALS AND METHODS: Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test.
RESULTS: The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29).
CONCLUSION: The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
A comparative study between xerographic, computer-assisted overlay generation and animated-superimposition methods in bite mark analyses

Tai MW, Chong ZF, Asif MK, Rahmat RA, Nambiar P

Leg Med (Tokyo), 2016 Sep;22:42-8.
PMID: 27591538 DOI: 10.1016/j.legalmed.2016.07.009

This study was to compare the suitability and precision of xerographic and computer-assisted methods for bite mark investigations. Eleven subjects were asked to bite on their forearm and the bite marks were photographically recorded. Alginate impressions of the subjects' dentition were taken and their casts were made using dental stone. The overlays generated by xerographic method were obtained by photocopying the subjects' casts and the incisal edge outlines were then transferred on a transparent sheet. The bite mark images were imported into Adobe Photoshop® software and printed to life-size. The bite mark analyses using xerographically generated overlays were done by comparing an overlay to the corresponding printed bite mark images manually. In computer-assisted method, the subjects' casts were scanned into Adobe Photoshop®. The bite mark analyses using computer-assisted overlay generation were done by matching an overlay and the corresponding bite mark images digitally using Adobe Photoshop®. Another comparison method was superimposing the cast images with corresponding bite mark images employing the Adobe Photoshop® CS6 and GIF-Animator©. A score with a range of 0-3 was given during analysis to each precision-determining criterion and the score was increased with better matching. The Kruskal Wallis H test showed significant difference between the three sets of data (H=18.761, p<0.05). In conclusion, bite mark analysis using the computer-assisted animated-superimposition method was the most accurate, followed by the computer-assisted overlay generation and lastly the xerographic method. The superior precision contributed by digital method is discernible despite the human skin being a poor recording medium of bite marks.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism

Usman OL, Muniyandi RC, Omar K, Mohamad M

PLoS One, 2021;16(2):e0245579.
PMID: 33630876 DOI: 10.1371/journal.pone.0245579

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels' intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images

Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibé D, Meriaudeau F

Biomed Eng Online, 2017 Jun 07;16(1):68.
PMID: 28592309 DOI: 10.1186/s12938-017-0352-9

BACKGROUND: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers.
METHODS: The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations.
RESULTS AND CONCLUSION: Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging

Ibrahim MF, Ahmad Sa'ad FS, Zakaria A, Md Shakaff AY

Sensors (Basel), 2016 Oct 27;16(11).
PMID: 27801799

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Imaging of skin birefringence for human scar assessment using polarization-sensitive optical coherence tomography aided by vascular masking

Gong P, Chin L, Es'haghian S, Liew YM, Wood FM, Sampson DD, et al.

J Biomed Opt, 2014 Dec;19(12):126014.
PMID: 25539060 DOI: 10.1117/1.JBO.19.12.126014

We demonstrate the in vivo assessment of human scars by parametric imaging of birefringence using polarization-sensitive optical coherence tomography (PS-OCT). Such in vivo assessment is subject to artifacts in the detected birefringence caused by scattering from blood vessels. To reduce these artifacts, we preprocessed the PS-OCT data using a vascular masking technique. The birefringence of the remaining tissue regions was then automatically quantified. Results from the scars and contralateral or adjacent normal skin of 13 patients show a correspondence of birefringence with scar type: the ratio of birefringence of hypertrophic scars to corresponding normal skin is 2.2 ± 0.2 (mean ± standard deviation ), while the ratio of birefringence of normotrophic scars to normal skin is 1.1 ± 0.4 . This method represents a new clinically applicable means for objective, quantitative human scar assessment.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Preoperative high resolution CT and MR imaging in cochlear implantation

Abdullah A, Mahmud MR, Maimunah A, Zulfiqar MA, Saim L, Mazlan R

Ann Acad Med Singap, 2003 Jul;32(4):442-5.
PMID: 12968546

INTRODUCTION: Accurate preoperative imaging of the temporal bone in patients receiving cochlear implants is important. High resolution computed tomography (HRCT) and magnetic resonance (MR) imaging are the 2 preoperative imaging modalities that provide critical information on abnormalities of the otic capsule, pneumatisation of the mastoid, middle ear abnormalities, cochlear ducts patency and presence of cochlear nerve.
MATERIALS AND METHODS: The HRCT and MR imaging in 46 cochlear implant patients in our department were reviewed.
RESULTS: Majority of our patients [34 patients (73.9%)] showed normal HRCT of the temporal bone; 5 (10.9%) patients had labyrinthitis ossificans, 2 (4.3%) had Mondini's abnormality and 2 (4.3%) had middle ear effusion. One patient each had high jugular bulb, hypoplasia of the internal auditory canal and single cochlear cavity, respectively.
CONCLUSION: The above findings contribute significantly to our surgical decisions regarding candidacy for surgery, side selection and surgical technique in cochlear implantation.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

AlDahoul N, Md Sabri AQ, Mansoor AM

Comput Intell Neurosci, 2018;2018:1639561.
PMID: 29623089 DOI: 10.1155/2018/1639561

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM's training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU).

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Digital image processing and analysis for activated sludge wastewater treatment

Khan MB, Lee XY, Nisar H, Ng CA, Yeap KH, Malik AS

Adv Exp Med Biol, 2015;823:227-48.
PMID: 25381111 DOI: 10.1007/978-3-319-10984-8_13

Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Estimating body related soft biometric traits in video frames

Arigbabu OA, Ahmad SM, Adnan WA, Yussof S, Iranmanesh V, Malallah FL

ScientificWorldJournal, 2014;2014:460973.
PMID: 25121120 DOI: 10.1155/2014/460973

Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Fulltext Reducing scanning electron microscope charging by using exponential contrast stretching technique on post-processing images

Sim KS, Tan YY, Lai MA, Tso CP, Lim WK

J Microsc, 2010 Apr 1;238(1):44-56.
PMID: 20384837 DOI: 10.1111/j.1365-2818.2009.03328.x

An exponential contrast stretching (ECS) technique is developed to reduce the charging effects on scanning electron microscope images. Compared to some of the conventional histogram equalization methods, such as bi-histogram equalization and recursive mean-separate histogram equalization, the proposed ECS method yields better image compensation. Diode sample chips with insulating and conductive surfaces are used as test samples to evaluate the efficiency of the developed algorithm. The algorithm is implemented in software with a frame grabber card, forming the front-end video capture element.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Rahman H, Khan AR, Sadiq T, Farooqi AH, Khan IU, Lim WH

Tomography, 2023 Dec 05;9(6):2158-2189.
PMID: 38133073 DOI: 10.3390/tomography9060169

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Assessing regional left ventricular thickening dysfunction and dyssynchrony via personalized modeling and 3D wall thickness measurements for acute myocardial infarction

Khalid A, Lim E, Chan BT, Abdul Aziz YF, Chee KH, Yap HJ, et al.

J Magn Reson Imaging, 2019 04;49(4):1006-1019.
PMID: 30211445 DOI: 10.1002/jmri.26302

BACKGROUND: Existing clinical diagnostic and assessment methods could be improved to facilitate early detection and treatment of cardiac dysfunction associated with acute myocardial infarction (AMI) to reduce morbidity and mortality.
PURPOSE: To develop 3D personalized left ventricular (LV) models and thickening assessment framework for assessing regional wall thickening dysfunction and dyssynchrony in AMI patients.
STUDY TYPE: Retrospective study, diagnostic accuracy.
SUBJECTS: Forty-four subjects consisting of 15 healthy subjects and 29 AMI patients.
FIELD STRENGTH/SEQUENCE: 1.5T/steady-state free precession cine MRI scans; LGE MRI scans.
ASSESSMENT: Quantitative thickening measurements across all cardiac phases were correlated and validated against clinical evaluation of infarct transmurality by an experienced cardiac radiologist based on the American Heart Association (AHA) 17-segment model.
STATISTICAL TEST: Nonparametric 2-k related sample-based Kruskal-Wallis test; Mann-Whitney U-test; Pearson's correlation coefficient.
RESULTS: Healthy LV wall segments undergo significant wall thickening (P 50% transmurality) underwent remarkable wall thinning during contraction (thickening index [TI] = 1.46 ± 0.26 mm) as opposed to healthy myocardium (TI = 4.01 ± 1.04 mm). For AMI patients, LV that showed signs of thinning were found to be associated with a significantly higher percentage of dyssynchrony as compared with healthy subjects (dyssynchrony index [DI] = 15.0 ± 5.0% vs. 7.5 ± 2.0%, P

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Identification and localization of fovea on colour fundus images using blur scales

Ganesan K, Acharya RU, Chua CK, Laude A

Proc Inst Mech Eng H, 2014 Sep;228(9):962-70.
PMID: 25234036 DOI: 10.1177/0954411914550847

Identification of retinal landmarks is an important step in the extraction of anomalies in retinal fundus images. In the current study, we propose a technique to identify and localize the position of macula and hence the fovea avascular zone, in colour fundus images. The proposed method, based on varying blur scales in images, is independent of the location of other anatomical landmarks present in the fundus images. Experimental results have been provided using the open database MESSIDOR by validating our segmented regions using the dice coefficient, with ground truth segmentation provided by a human expert. Apart from testing the images on the entire MESSIDOR database, the proposed technique was also validated using 50 normal and 50 diabetic retinopathy chosen digital fundus images from the same database. A maximum overlap accuracy of 89.6%-93.8% and locational accuracy of 94.7%-98.9% was obtained for identification and localization of the fovea.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Automated extraction of retinal vasculature

Tan JH, Acharya UR, Chua KC, Cheng C, Laude A

Med Phys, 2016 May;43(5):2311.
PMID: 27147343 DOI: 10.1118/1.4945413

The authors propose an algorithm that automatically extracts retinal vasculature and provides a simple measure to correct the extraction. The output of the method is a network of salient points, and blood vessels are drawn by connecting the salient points using a centripetal parameterized Catmull-Rom spline.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*

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