MyMedR

Displaying publications 121 - 140 of 169 in total

Abstract:

Sort:

A novel shape representation of the dental arch and its applications in some dentistry problems

Rijal OM, Abdullah NA, Isa ZM, Davaei FA, Noor NM, Tawfiq OF

Annu Int Conf IEEE Eng Med Biol Soc, 2011;2011:5092-5.
PMID: 22255484 DOI: 10.1109/IEMBS.2011.6091261

Standardized digital images of maxillary dental casts of 47 subjects were analyzed using MATLAB software whereby the two hamular notches and the incisive papilla defines the Cartesian vertical and horizontal axes, as well as the origin. The angle and length of the midpoints of the anterior teeth, mesiobuccal and distobuccal cusp of the posterior teeth were measured from the origin and denoted as θ(1), …, θ(18) and l(1), …, l(18) respectively. These values were collectively used to represent the shape of each dental cast. Clustering and principal component analyses were employed to find possible groups of dental arches using the above measure of shape. The main result of this study is that the 3 groups of dental arch shape may be represented by the novel feature vector v(k) = (θ(k)(1), l(k)(1), θ(k)(3), l(k)(3), θ(k)(5), l(k)(5), θ(k)(13), l(k)(13)), k = 1, 2, 3. Knowledge of v(k) implies three impression trays should be sufficient in a particular prosthetic dentistry application for Malaysian patients. Further, given that v(k) are accurately measured they may be potential candidates as evidence in specific application of forensic dentistry.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Improved biliary detection and diagnosis through intelligent machine analysis

Logeswaran R

Comput Methods Programs Biomed, 2012 Sep;107(3):404-12.
PMID: 21194781 DOI: 10.1016/j.cmpb.2010.12.002

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds

Reza AW, Eswaran C, Hati S

J Med Syst, 2009 Feb;33(1):73-80.
PMID: 19238899

The detection of bright objects such as optic disc (OD) and exudates in color fundus images is an important step in the diagnosis of eye diseases such as diabetic retinopathy and glaucoma. In this paper, a novel approach to automatically segment the OD and exudates is proposed. The proposed algorithm makes use of the green component of the image and preprocessing steps such as average filtering, contrast adjustment, and thresholding. The other processing techniques used are morphological opening, extended maxima operator, minima imposition, and watershed transformation. The proposed algorithm is evaluated using the test images of STARE and DRIVE databases with fixed and variable thresholds. The images drawn by human expert are taken as the reference images. The proposed method yields sensitivity values as high as 96.7%, which are better than the results reported in the literature.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
Entrance surface dose and image quality: comparison of adult chest and abdominal X-ray examinations in general practitioner clinics, public and private hospitals in Malaysia

Hambali AS, Ng KH, Abdullah BJ, Wang HB, Jamal N, Spelic DC, et al.

Radiat Prot Dosimetry, 2009 Jan;133(1):25-34.
PMID: 19223292 DOI: 10.1093/rpd/ncp007

This study was undertaken to compare the entrance surface dose (ESD) and image quality of adult chest and abdominal X-ray examinations conducted at general practitioner (GP) clinics, and public and private hospitals in Malaysia. The surveyed facilities were randomly selected within a given category (28 GP clinics, 20 public hospitals and 15 private hospitals). Only departmental X-ray units were involved in the survey. Chest examinations were done at all facilities, while only hospitals performed abdominal examinations. This study used the x-ray attenuation phantoms and protocols developed for the Nationwide Evaluation of X-ray Trends (NEXT) survey program in the United States. The ESD was calculated from measurements of exposure and clinical geometry. An image quality test tool was used to evaluate the low-contrast detectability and high-contrast detail performance under typical clinical conditions. The median ESD value for the adult chest X-ray examination was the highest (0.25 mGy) at GP clinics, followed by private hospitals (0.22 mGy) and public hospitals (0.17 mGy). The median ESD for the adult abdominal X-ray examination at public hospitals (3.35 mGy) was higher than that for private hospitals (2.81 mGy). Results of image quality assessment for the chest X-ray examination show that all facility types have a similar median spatial resolution and low-contrast detectability. For the abdominal X-ray examination, public hospitals have a similar median spatial resolution but larger low-contrast detectability compared with private hospitals. The results of this survey clearly show that there is room for further improvement in performing chest and abdominal X-ray examinations in Malaysia.

Matched MeSH terms: Image Interpretation, Computer-Assisted*
Area assessment of psoriasis lesion for PASI scoring

Ihtatho D, Fadzil MH, Affandi AM, Hussein SH

Annu Int Conf IEEE Eng Med Biol Soc, 2007;2007:3446-9.
PMID: 18002738

Psoriasis is a skin disorder which is caused by genetic fault. There is no cure for psoriasis, however, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, PASI (Psoriasis Area and Severity Index) which is the current gold standard method is used to measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the plaques. However, the calculation of PASI can be tedious and subjective. In this work, we develop a computer vision method that determines one of the PASI parameter, the lesion area. The method isolates healthy (or healed) skin areas from lesion areas by analyzing the hue and chroma information in the CIE L*a*b* colour space. Centroids of healthy skin and psoriasis in the hue-chroma space are determined from selected sample. Euclidean distance of all pixels from each centroid is calculated. Each pixel is assigned to the class with minimum Euclidean distance. The study involves patients from three different ethnic origins having different skin tones. Results obtained show that the proposed method is comparable to the dermatologist visual approach.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Determination of skin repigmentation progression

Nugroho H, Fadzil MH, Yap VV, Norashikin S, Suraiya HH

Annu Int Conf IEEE Eng Med Biol Soc, 2007;2007:3442-5.
PMID: 18002737

In this paper, we describe an image processing scheme to analyze and determine areas of skin that have undergone repigmentation in particular, during the treatment of vitiligo. In vitiligo cases, areas of skin become pale or white due to the lack of skin pigment called melanin. Vitiligo treatment causes skin repigmentation resulting in a normal skin color. However, it is difficult to determine and quantify the amount of repigmentation visually during treatment because the repigmentation progress is slow and moreover changes in skin color can only be discerned over a longer time frame typically 6 months. Here, we develop a digital image analysis scheme that can identify and determine vitiligo skin areas and repigmentation progression on a shorter time period. The technique is based on principal component analysis and independent component analysis which converts the RGB skin image into a skin image that represent skin areas due to melanin and haemoglobin only, followed by segmentation process. Vitiligo skin lesions are identified as skin areas that lack melanin (non-melanin areas). In the initial studies of 4 patients, the method has been able to quantify repigmentation in vitiligo lesion. Hence it is now possible to determine repigmentation progression objectively and treatment efficacy on a shorter time cycle.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Clinical evaluation of watermarked medical images

Zain JM, Fauzi AM, Aziz AA

Conf Proc IEEE Eng Med Biol Soc, 2007 10 20;2006:5459-62.
PMID: 17946306

Digital watermarking medical images provides security to the images. The purpose of this study was to see whether digitally watermarked images changed clinical diagnoses when assessed by radiologists. We embedded 256 bits watermark to various medical images in the region of non-interest (RONI) and 480K bits in both region of interest (ROI) and RONI. Our results showed that watermarking medical images did not alter clinical diagnoses. In addition, there was no difference in image quality when visually assessed by the medical radiologists. We therefore concluded that digital watermarking medical images were safe in terms of preserving image quality for clinical purposes.

Matched MeSH terms: Image Interpretation, Computer-Assisted*
Quantitative assessment of breast density from digitized mammograms into Tabar's patterns

Jamal N, Ng KH, Looi LM, McLean D, Zulfiqar A, Tan SP, et al.

Phys Med Biol, 2006 Nov 21;51(22):5843-57.
PMID: 17068368

We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r2 = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk.

Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
Body surface area measurement and soft clustering for PASI area assessment

Hani AF, Prakasa E, Nugroho H, Affandi AM, Hussein SH

Annu Int Conf IEEE Eng Med Biol Soc, 2012;2012:4398-401.
PMID: 23366902 DOI: 10.1109/EMBC.2012.6346941

Psoriasis is a common skin disorder with a prevalence of 0.6 - 4.8% around the world. The most common is plaques psoriasis and it appears as red scaling plaques. Psoriasis is incurable but treatable in a long term treatment. Although PASI (Psoriasis Area and Severity Index) scoring is recognised as gold standard for psoriasis assessment, this method is still influenced by inter and intra-rater variation. An imaging and analysis system called α-PASI is developed to perform PASI scoring objectively. Percentage of lesion area to the body surface area is one of PASI parameter. In this paper, enhanced imaging methods are developed to improve the determination of body surface area (BSA) and lesion area. BSA determination method has been validated on medical mannequin. BSA accuracies obtained at four body regions are 97.80% (lower limb), 92.41% (trunk), 87.72% (upper limb), and 83.82% (head). By applying fuzzy c-means clustering algorithm, the membership functions of lesions area for PASI area scoring have been determined. Performance of scoring result has been tested with double assessment by α-PASI area algorithm on body region images from 46 patients. Kappa coefficients for α-PASI system are greater than or equal to 0.72 for all body regions (Head - 0.76, Upper limb - 0.81, Trunk - 0.85, Lower limb - 0.72). The overall kappa coefficient for the α-PASI area is 0.80 that can be categorised as substantial agreement. This shows that the α-PASI area system has a high reliability and can be used in psoriasis area assessment.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients

Saba L, Than JC, Noor NM, Rijal OM, Kassim RM, Yunus A, et al.

J Med Syst, 2016 Jun;40(6):142.
PMID: 27114353 DOI: 10.1007/s10916-016-0504-7

Human interaction has become almost mandatory for an automated medical system wishing to be accepted by clinical regulatory agencies such as Food and Drug Administration. Since this interaction causes variability in the gathered data, the inter-observer and intra-observer variability must be analyzed in order to validate the accuracy of the system. This study focuses on the variability from different observers that interact with an automated lung delineation system that relies on human interaction in the form of delineation of the lung borders. The database consists of High Resolution Computed Tomography (HRCT): 15 normal and 81 diseased patients' images taken retrospectively at five levels per patient. Three observers manually delineated the lungs borders independently and using software called ImgTracer™ (AtheroPoint™, Roseville, CA, USA) to delineate the lung boundaries in all five levels of 3-D lung volume. The three observers consisted of Observer-1: lesser experienced novice tracer who is a resident in radiology under the guidance of radiologist, whereas Observer-2 and Observer-3 are lung image scientists trained by lung radiologist and biomedical imaging scientist and experts. The inter-observer variability can be shown by comparing each observer's tracings to the automated delineation and also by comparing each manual tracing of the observers with one another. The normality of the tracings was tested using D'Agostino-Pearson test and all observers tracings showed a normal P-value higher than 0.05. The analysis of variance (ANOVA) test between three observers and automated showed a P-value higher than 0.89 and 0.81 for the right lung (RL) and left lung (LL), respectively. The performance of the automated system was evaluated using Dice Similarity Coefficient (DSC), Jaccard Index (JI) and Hausdorff (HD) Distance measures. Although, Observer-1 has lesser experience compared to Obsever-2 and Obsever-3, the Observer Deterioration Factor (ODF) shows that Observer-1 has less than 10% difference compared to the other two, which is under acceptable range as per our analysis. To compare between observers, this study used regression plots, Bland-Altman plots, two tailed T-test, Mann-Whiney, Chi-Squared tests which showed the following P-values for RL and LL: (i) Observer-1 and Observer-3 were: 0.55, 0.48, 0.29 for RL and 0.55, 0.59, 0.29 for LL; (ii) Observer-1 and Observer-2 were: 0.57, 0.50, 0.29 for RL and 0.54, 0.59, 0.29 for LL; (iii) Observer-2 and Observer-3 were: 0.98, 0.99, 0.29 for RL and 0.99, 0.99, 0.29 for LL. Further, CC and R-squared coefficients were computed between observers which came out to be 0.9 for RL and LL. All three observers however manage to show the feature that diseased lungs are smaller than normal lungs in terms of area.

Matched MeSH terms: Image Interpretation, Computer-Assisted*
Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions

Raghavendra U, Rajendra Acharya U, Gudigar A, Hong Tan J, Fujita H, Hagiwara Y, et al.

Ultrasonics, 2017 05;77:110-120.
PMID: 28219805 DOI: 10.1016/j.ultras.2017.02.003

Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features

Acharya UR, Raghavendra U, Koh JEW, Meiburger KM, Ciaccio EJ, Hagiwara Y, et al.

Comput Methods Programs Biomed, 2018 Nov;166:91-98.
PMID: 30415722 DOI: 10.1016/j.cmpb.2018.10.006

BACKGROUND AND OBJECTIVE: Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Ultrasound-based elastography is a promising tool to measure tissue elasticity in real time; however, this technology requires an upgrade of the ultrasound system and software. In this study, a novel computer-aided diagnosis tool is proposed to automatically detect and classify the various stages of liver fibrosis based upon conventional B-mode ultrasound images.
METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.
RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.
CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
Fulltext Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG)

Al-Faris AQ, Ngah UK, Isa NA, Shuaib IL

J Digit Imaging, 2014 Feb;27(1):133-44.
PMID: 24100762 DOI: 10.1007/s10278-013-9640-5

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap (p = 0.0002), misclassification rate (p = 0.045), true negative fraction (p = 0.0001) and sum of true volume fraction (p = 0.0001).

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline

Loh KB, Ramli N, Tan LK, Roziah M, Rahmat K, Ariffin H

Eur Radiol, 2012 Jul;22(7):1413-26.
PMID: 22434420 DOI: 10.1007/s00330-012-2396-3

OBJECTIVES: The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas.
METHODS: Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction.
RESULTS: DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued.
CONCLUSION: DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data.
KEY POINTS: Diffusion tensor imaging outperforms conventional MRI in depicting white matter maturation. • DTI will become an important clinical tool for diagnosing paediatric neurological diseases. • DTI appears especially helpful for developmental abnormalities, tumours and white matter disease. • An automated processing pipeline assists quantitative analysis of high throughput DTI data.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
A videoendoscopic evaluation of the upper airway in South East Asian adults with obstructive sleep apnea

Abdullah B, Rajet KA, Abd Hamid SS, Mohammad WM

Sleep Breath, 2011 Dec;15(4):747-54.
PMID: 20957444 DOI: 10.1007/s11325-010-0431-7

OBJECTIVES: We aimed to evaluate the severity of upper airway obstruction at the retropalatal and retroglossal regions in obstructive sleep apnea (OSA) patients.
METHODOLOGY: This is a descriptive cross-sectional study at the Sleep Clinic, Department of Otorhinolaryngology-Head and Neck Surgery. Flexible nasopharyngolaryngoscopy was performed in seated erect and supine position. Retropalatal and retroglossal regions were continuously recorded during quiet breathing and Mueller's maneuver in both positions. Captured images were measured using Scion Image software and narrowing rate was calculated. Level of each site was classified based on Fujita classification and severity of obstruction using Sher scoring system for Mueller's maneuver.
RESULTS: A total of 59 patients participated in this study. Twenty-nine (49.2%) participants had type 1 (retropalatal) obstruction, 23 (38.9%) had type 2 (retropalatal and retroglossal), and seven (11.9%) in type 3 (retroglossal) obstruction. Fifty (84.7%) of the patients have severe obstruction at the retropalatal region in supine position (SRP) followed by 35 (59.3%) at retropalatal region in erect position (ERP), 27 (45.8%) at retroglossal region in supine position (SRG) and eight (13.5%) at retroglossal region in erect position (ERG). The average oxygen saturation showed significant association in ERP (P = 0.012) and SRP (P < 0.001), but not significant in ERG and SRG.
CONCLUSIONS: Videoendoscopy utilizing flexible nasopharyngolaryngoscopy and Scion Image software is reliable, minimally invasive, and useful as an office procedure in evaluating the multilevel obstruction of upper airway in OSA patients. The retropalatal region has more severe obstruction compared with retroglossal region either in erect or supine position.

Matched MeSH terms: Image Interpretation, Computer-Assisted/instrumentation
An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding

Saleh MD, Eswaran C

Comput Methods Biomech Biomed Engin, 2012;15(5):517-25.
PMID: 21331960 DOI: 10.1080/10255842.2010.545949

Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
Brain white and gray matter anatomy of MRI segmentation based on tissue evaluation

Salih QA, Ramli AR, Mahmud R, Wirza R

MedGenMed, 2005;7(2):1.
PMID: 16369380

Different approaches to gray and white matter measurements in magnetic resonance imaging (MRI) have been studied. For clinical use, the estimated values must be reliable and accurate when, unfortunately, many techniques fail on these criteria in an unrestricted clinical environment. A recent method for tissue clusterization in MRI analysis has the advantage of great simplicity, and it takes the account of partial volume effects. In this study, we will evaluate the intensity of MR sequences known as T1-weighted images in an axial sliced section. Intensity group clustering algorithms are proposed to achieve further diagnosis for brain MRI, which has been hardly studied. Subjective study has been suggested to evaluate the clustering group intensity in order to obtain the best diagnosis as well as better detection for the suspected cases. This technique makes use of image tissue biases of intensity value pixels to provide 2 regions of interest as techniques. Moreover, the original mathematic solution could still be used with a specific set of modern sequences. There are many advantages to generalize the solution, which give far more scope for application and greater accuracy.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Semi-quantitative and qualitative assessment of breast ultrasound elastography in differentiating between malignant and benign lesions

Alhabshi SM, Rahmat K, Abdul Halim N, Aziz S, Radhika S, Gan GC, et al.

Ultrasound Med Biol, 2013 Apr;39(4):568-78.
PMID: 23384468 DOI: 10.1016/j.ultrasmedbio.2012.10.016

The purpose of this study was to evaluate the diagnostic value of qualitative and semi-quantitative assessment of ultrasound elastography in differentiating between benign and malignant breast lesions. This prospective study was conducted in two tertiary medical centers. Consecutive B-mode ultrasound and real-time elastographic images were obtained for 67 malignant and 101 benign breast lesions in 168 women. Four experienced radiologists analyzed B-mode ultrasound alone and B-mode ultrasound combined with elastography independently. Conventional ultrasound findings were classified according to the American College of Radiology Breast Imaging Reporting and Data System classification. The elastographic assessment was based on qualitative and semi-quantitative parameters (i.e., strain pattern, width ratio, strain ratio). The sensitivity and specificity of combined elastography and conventional ultrasound were significantly higher than that of conventional ultrasound alone. The sensitivity, specificity, positive predictive value and negative predictive value was 97%, 61.4%, 62.5% and 96.8%, respectively, for conventional ultrasound and 100%, 93%, 99% and 90%, respectively, for combined technique. The semi-quantitative assessment with strain ratio and width ratio in elastography were the most useful parameters in differentiating between benign and malignant breast lesions. Cut-off point values for width ratio of more than 1.1 and strain ratio of more than 5.6 showed a high predictive value of malignancy with specificities of 84% and 76%, respectively (p

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
Fulltext Determination of an optimally sensitive and specific chemical exchange saturation transfer MRI quantification metric in relevant biological phantoms

Ray KJ, Larkin JR, Tee YK, Khrapitchev AA, Karunanithy G, Barber M, et al.

NMR Biomed, 2016 11;29(11):1624-1633.
PMID: 27686882 DOI: 10.1002/nbm.3614

The purpose of this study was to develop realistic phantom models of the intracellular environment of metastatic breast tumour and naïve brain, and using these models determine an analysis metric for quantification of CEST MRI data that is sensitive to only labile proton exchange rate and concentration. The ability of the optimal metric to quantify pH differences in the phantoms was also evaluated. Novel phantom models were produced, by adding perchloric acid extracts of either metastatic mouse breast carcinoma cells or healthy mouse brain to bovine serum albumin. The phantom model was validated using 1 H NMR spectroscopy, then utilized to determine the sensitivity of CEST MRI to changes in pH, labile proton concentration, T1 time and T2 time; six different CEST MRI analysis metrics (MTRasym , APT*, MTRRex , AREX and CESTR* with and without T1 /T2 compensation) were compared. The new phantom models were highly representative of the in vivo intracellular environment of both tumour and brain tissue. Of the analysis methods compared, CESTR* with T1 and T2 time compensation was optimally specific to changes in the CEST effect (i.e. minimal contamination from T1 or T2 variation). In phantoms with identical protein concentrations, pH differences between phantoms could be quantified with a mean accuracy of 0.6 pH units. We propose that CESTR* with T1 and T2 time compensation is the optimal analysis method for these phantoms. Analysis of CEST MRI data with T1 /T2 time compensated CESTR* is reproducible between phantoms, and its application in vivo may resolve the intracellular alkalosis associated with breast cancer brain metastases without the need for exogenous contrast agents.

Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
Fulltext Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Ong SQ, Ahmad H, Nair G, Isawasan P, Majid AHA

Sci Rep, 2021 05 10;11(1):9908.
PMID: 33972645 DOI: 10.1038/s41598-021-89365-3

Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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

Filters

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links