Displaying publications 61 - 80 of 113 in total

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  1. Blood vessel segmentation in color fundus images based on regional and Hessian features
    Shah SAA, Tang TB, Faye I, Laude A
    Graefes Arch Clin Exp Ophthalmol, 2017 Aug;255(8):1525-1533.
    PMID: 28474130 DOI: 10.1007/s00417-017-3677-y
    PURPOSE: To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis.

    METHODS: Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel.

    RESULTS: The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy.

    CONCLUSIONS: Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.

    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  2. Knee cartilage segmentation and thickness computation from ultrasound images
    Faisal A, Ng SC, Goh SL, Lai KW
    Med Biol Eng Comput, 2018 Apr;56(4):657-669.
    PMID: 28849317 DOI: 10.1007/s11517-017-1710-2
    Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen's κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  3. Fulltext An Overview of Deep Learning Methods for Left Ventricle Segmentation
    Shoaib MA, Chuah JH, Ali R, Hasikin K, Khalil A, Hum YC, et al.
    Comput Intell Neurosci, 2023;2023:4208231.
    PMID: 36756163 DOI: 10.1155/2023/4208231
    Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  4. Fulltext Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network
    Abisha S, Mutawa AM, Murugappan M, Krishnan S
    PLoS One, 2023;18(4):e0284021.
    PMID: 37018344 DOI: 10.1371/journal.pone.0284021
    Different diseases are observed in vegetables, fruits, cereals, and commercial crops by farmers and agricultural experts. Nonetheless, this evaluation process is time-consuming, and initial symptoms are primarily visible at microscopic levels, limiting the possibility of an accurate diagnosis. This paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN). We collected 1100 images of brinjal leaf disease that were caused by five different species (Pseudomonas solanacearum, Cercospora solani, Alternaria melongenea, Pythium aphanidermatum, and Tobacco Mosaic Virus) and 400 images of healthy leaves from India's agricultural form. First, the original plant leaf is preprocessed by a Gaussian filter to reduce the noise and improve the quality of the image through image enhancement. A segmentation method based on expectation and maximization (EM) is then utilized to segment the leaf's-diseased regions. Next, the discrete Shearlet transform is used to extract the main features of the images such as texture, color, and structure, which are then merged to produce vectors. Lastly, DCNN and RBFNN are used to classify brinjal leaves based on their disease types. The DCNN achieved a mean accuracy of 93.30% (with fusion) and 76.70% (without fusion) compared to the RBFNN (82%-without fusion, 87%-with fusion) in classifying leaf diseases.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  5. Fulltext A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis
    Kamarudin ND, Ooi CY, Kawanabe T, Odaguchi H, Kobayashi F
    J Healthc Eng, 2017;2017:7460168.
    PMID: 29065640 DOI: 10.1155/2017/7460168
    In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  6. A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI
    Al-Masni MA, Lee S, Al-Shamiri AK, Gho SM, Choi YH, Kim DH
    Comput Biol Med, 2023 Feb;153:106553.
    PMID: 36641933 DOI: 10.1016/j.compbiomed.2023.106553
    Patient movement during Magnetic Resonance Imaging (MRI) scan can cause severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several echoes are typically measured during a single repetition period, where the earliest echoes show less contrast between various tissues, while the later echoes are more susceptible to artifacts and signal dropout. In this paper, we propose a knowledge interaction paradigm that jointly learns feature details from multiple distorted echoes by sharing their knowledge with unified training parameters, thereby simultaneously reducing motion artifacts of all echoes. This is accomplished by developing a new scheme that boosts a Single Encoder with Multiple Decoders (SEMD), which assures that the generated features not only get fused but also learned together. We called the proposed method Knowledge Interaction Learning between Multi-Echo data (KIL-ME-based SEMD). The proposed KIL-ME-based SEMD allows to share information and gain an understanding of the correlations between the multiple echoes. The main purpose of this work is to correct the motion artifacts and maintain image quality and structure details of all motion-corrupted echoes towards generating high-resolution susceptibility enhanced contrast images, i.e., SWI, using a weighted average of multi-echo motion-corrected acquisitions. We also compare various potential strategies that might be used to address the problem of reducing artifacts in multi-echoes data. The experimental results demonstrate the feasibility and effectiveness of the proposed method, reducing the severity of motion artifacts and improving the overall clinical image quality of all echoes with their associated SWI maps. Significant improvement of image quality is observed using both motion-simulated test data and actual volunteer data with various motion severity strengths. Eventually, by enhancing the overall image quality, the proposed network can increase the effectiveness of the physicians' capability to evaluate and correctly diagnose brain MR images.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  7. Intelligent microscopic approach for identification and recognition of citrus deformities
    Safdar A, Khan MA, Shah JH, Sharif M, Saba T, Rehman A, et al.
    Microsc Res Tech, 2019 Sep;82(9):1542-1556.
    PMID: 31209970 DOI: 10.1002/jemt.23320
    Plant diseases are accountable for economic losses in an agricultural country. The manual process of plant diseases diagnosis is a key challenge from last one decade; therefore, researchers in this area introduced automated systems. In this research work, automated system is proposed for citrus fruit diseases recognition using computer vision technique. The proposed method incorporates five fundamental steps such as preprocessing, disease segmentation, feature extraction and reduction, fusion, and classification. The noise is being removed followed by a contrast stretching procedure in the very first phase. Later, watershed method is applied to excerpt the infectious regions. The shape, texture, and color features are subsequently computed from these infection regions. In the fourth step, reduced features are fused using serial-based approach followed by a final step of classification using multiclass support vector machine. For dimensionality reduction, principal component analysis is utilized, which is a statistical procedure that enforces an orthogonal transformation on a set of observations. Three different image data sets (Citrus Image Gallery, Plant Village, and self-collected) are combined in this research to achieving a classification accuracy of 95.5%. From the stats, it is quite clear that our proposed method outperforms several existing methods with greater precision and accuracy.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  8. Radiation dose and diagnostic image quality associated with iterative reconstruction in coronary CT angiography: A systematic review
    Abdullah KA, McEntee MF, Reed W, Kench PL
    J Med Imaging Radiat Oncol, 2016 Aug;60(4):459-68.
    PMID: 27241506 DOI: 10.1111/1754-9485.12473
    The aim of this systematic review is to evaluate the radiation dose reduction achieved using iterative reconstruction (IR) compared to filtered back projection (FBP) in coronary CT angiography (CCTA) and assess the impact on diagnostic image quality. A systematic search of seven electronic databases was performed to identify all studies using a developed keywords strategy. A total of 14 studies met the criteria and were included in a review analysis. The results showed that there was a significant reduction in radiation dose when using IR compared to FBP (P  0.05). The mean ± SD difference of image noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR) were 1.05 ± 1.29 HU, 0.88 ± 0.56 and 0.63 ± 1.83 respectively. The mean ± SD percentages of overall image quality scores were 71.79 ± 12.29% (FBP) and 67.31 ± 22.96% (IR). The mean ± SD percentages of coronary segment analysis were 95.43 ± 2.57% (FBP) and 97.19 ± 2.62% (IR). In conclusion, this review analysis shows that CCTA with the use of IR leads to a significant reduction in radiation dose as compared to the use of FBP. Diagnostic image quality of IR at reduced dose (30-41%) is comparable to FBP at standard dose in the diagnosis of CAD.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  9. Auto-shape lossless compression of pharynx and esophagus fluoroscopic images
    Arif AS, Mansor S, Logeswaran R, Karim HA
    J Med Syst, 2015 Feb;39(2):5.
    PMID: 25628161 DOI: 10.1007/s10916-015-0200-z
    The massive number of medical images produced by fluoroscopic and other conventional diagnostic imaging devices demand a considerable amount of space for data storage. This paper proposes an effective method for lossless compression of fluoroscopic images. The main contribution in this paper is the extraction of the regions of interest (ROI) in fluoroscopic images using appropriate shapes. The extracted ROI is then effectively compressed using customized correlation and the combination of Run Length and Huffman coding, to increase compression ratio. The experimental results achieved show that the proposed method is able to improve the compression ratio by 400 % as compared to that of traditional methods.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  10. FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities
    Siddiqui MF, Reza AW, Shafique A, Omer H, Kanesan J
    Magn Reson Imaging, 2017 12;44:82-91.
    PMID: 28855113 DOI: 10.1016/j.mri.2017.08.005
    Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10-2) and comparable artefact power (<9×10-4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  11. Fulltext Reduction of Motion Artifacts in the Recovery of Undersampled DCE MR Images Using Data Binning and L+S Decomposition
    Bilal M, Anis H, Khan N, Qureshi I, Shah J, Kadir KA
    Biomed Res Int, 2019;2019:6139785.
    PMID: 31119178 DOI: 10.1155/2019/6139785
    Background: Motion is a major source of blurring and ghosting in recovered MR images. It is more challenging in Dynamic Contrast Enhancement (DCE) MRI because motion effects and rapid intensity changes in contrast agent are difficult to distinguish from each other.

    Material and Methods: In this study, we have introduced a new technique to reduce the motion artifacts, based on data binning and low rank plus sparse (L+S) reconstruction method for DCE MRI. For Data binning, radial k-space data is acquired continuously using the golden-angle radial sampling pattern and grouped into various motion states or bins. The respiratory signal for binning is extracted directly from radially acquired k-space data. A compressed sensing- (CS-) based L+S matrix decomposition model is then used to reconstruct motion sorted DCE MR images. Undersampled free breathing 3D liver and abdominal DCE MR data sets are used to validate the proposed technique.

    Results: The performance of the technique is compared with conventional L+S decomposition qualitatively along with the image sharpness and structural similarity index. Recovered images are visually sharper and have better similarity with reference images.

    Conclusion: L+S decomposition provides improved MR images with data binning as preprocessing step in free breathing scenario. Data binning resolves the respiratory motion by dividing different respiratory positions in multiple bins. It also differentiates the respiratory motion and contrast agent (CA) variations. MR images recovered for each bin are better as compared to the method without data binning.

    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  12. Reproducibility of facial soft tissue landmarks on facial images captured on a 3D camera
    Othman SA, Ahmad R, Mericant AF, Jamaludin M
    Aust Orthod J, 2013 May;29(1):58-65.
    PMID: 23785939
    Fast and non-invasive systems of the three-dimensional (3D) technology are a recent trend in orthodontics. The reproducibility of facial landmarks is important so that 3D facial measurements are accurate and may-be applied clinically. The aim of this study is to evaluate the reproducibility of facial soft tissue landmarks using a non-invasive stereo-photogrammetry 3D camera.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  13. A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans
    Ibrahim RW, Hasan AM, Jalab HA
    Comput Methods Programs Biomed, 2018 Sep;163:21-28.
    PMID: 30119853 DOI: 10.1016/j.cmpb.2018.05.031
    BACKGROUND AND OBJECTIVES: The MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features' intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficulty that encounters the active contour segmentation is the boundary tracking which is controlled by minimization of energy function for segmentation. Hence, this study proposes a novel fractional Wright function (FWF) as a minimization of energy technique to improve the performance of active contour without edge method.

    METHOD: In this study, we implement FWF as an energy minimization function to replace the standard gradient-descent method as minimization function in Chan-Vese segmentation technique. The proposed FWF is used to find the boundaries of an object by controlling the inside and outside values of the contour. In this study, the objective evaluation is used to distinguish the differences between the processed segmented images and ground truth using a set of statistical parameters; true positive, true negative, false positive, and false negative.

    RESULTS: The FWF as a minimization of energy was successfully implemented on BRATS 2013 image dataset. The achieved overall average sensitivity score of the brain tumors segmentation was 94.8 ± 4.7%.

    CONCLUSIONS: The results demonstrate that the proposed FWF method minimized the energy function more than the gradient-decent method that was used in the original three-dimensional active contour without edge (3DACWE) method.

    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  14. Fulltext Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features
    Mousavi Kahaki SM, Nordin MJ, Ashtari AH, J Zahra S
    PLoS One, 2016;11(3):e0149710.
    PMID: 26985996 DOI: 10.1371/journal.pone.0149710
    An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics--such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient--are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  15. Fulltext Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method
    Gupta R, Elamvazuthi I, Dass SC, Faye I, Vasant P, George J, et al.
    Biomed Eng Online, 2014;13:157.
    PMID: 25471386 DOI: 10.1186/1475-925X-13-157
    Disorders of rotator cuff tendons results in acute pain limiting the normal range of motion for shoulder. Of all the tendons in rotator cuff, supraspinatus (SSP) tendon is affected first of any pathological changes. Diagnosis of SSP tendon using ultrasound is considered to be operator dependent with its accuracy being related to operator's level of experience.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  16. Fulltext Geometric feature descriptor and dissimilarity-based registration of remotely sensed imagery
    Kahaki SMM, Arshad H, Nordin MJ, Ismail W
    PLoS One, 2018;13(7):e0200676.
    PMID: 30024921 DOI: 10.1371/journal.pone.0200676
    Image registration of remotely sensed imagery is challenging, as complex deformations are common. Different deformations, such as affine and homogenous transformation, combined with multimodal data capturing can emerge in the data acquisition process. These effects, when combined, tend to compromise the performance of the currently available registration methods. A new image transform, known as geometric mean projection transform, is introduced in this work. As it is deformation invariant, it can be employed as a feature descriptor, whereby it analyzes the functions of all vertical and horizontal signals in local areas of the image. Moreover, an invariant feature correspondence method is proposed as a point matching algorithm, which incorporates new descriptor's dissimilarity metric. Considering the image as a signal, the proposed approach utilizes a square Eigenvector correlation (SEC) based on the Eigenvector properties. In our experiments on standard test images sourced from "Featurespace" and "IKONOS" datasets, the proposed method achieved higher average accuracy relative to that obtained from other state of the art image registration techniques. The accuracy of the proposed method was assessed using six standard evaluation metrics. Furthermore, statistical analyses, including t-test and Friedman test, demonstrate that the method developed as a part of this study is superior to the existing methods.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  17. Fulltext Round Randomized Learning Vector Quantization for Brain Tumor Imaging
    Sheikh Abdullah SN, Bohani FA, Nayef BH, Sahran S, Al Akash O, Iqbal Hussain R, et al.
    Comput Math Methods Med, 2016;2016:8603609.
    PMID: 27516807 DOI: 10.1155/2016/8603609
    Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  18. Fulltext Development of a kidney TeleUltrasound consultation system
    Hassan A, Ibrahim F
    J Digit Imaging, 2011 Apr;24(2):308-13.
    PMID: 20386951 DOI: 10.1007/s10278-010-9283-8
    This paper presents the development of kidney TeleUltrasound consultation system. The TeleUltrasound system provides an innovative design that aids the acquisition, archiving, and dissemination of medical data and information over the internet as its backbone. The system provides data sharing to allow remote collaboration, viewing, consultation, and diagnosis of medical data. The design is layered upon a standard known as Digital Imaging and Communication in Medicine (DICOM). The DICOM standard defines protocols for exchanging medical images and their associated data. The TeleUltrasound system is an integrated solution for retrieving, processing, and archiving images and providing data storage management using Structured Query Language (SQL) database. Creating a web-based interface is an additional advantage to achieve global accessibility of experts that will widely open the opportunity of greater examination and multiple consultations. This system is equipped with a high level of data security and its performance has been tested with white, black, and gray box techniques. And the result was satisfactory. The overall system has been evaluated by several radiologists in Malaysia, United Arab Emirates, and Sudan, the result is shown within this paper.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  19. 3D surface roughness measurement for scaliness scoring of psoriasis lesions
    Ahmad Fadzil MH, Prakasa E, Asirvadam VS, Nugroho H, Affandi AM, Hussein SH
    Comput Biol Med, 2013 Nov;43(11):1987-2000.
    PMID: 24054912 DOI: 10.1016/j.compbiomed.2013.08.009
    Psoriasis is an incurable skin disorder affecting 2-3% of the world population. The scaliness of psoriasis is a key assessment parameter of the Psoriasis Area and Severity Index (PASI). Dermatologists typically use visual and tactile senses in PASI scaliness assessment. However, the assessment can be subjective resulting in inter- and intra-rater variability in the scores. This paper proposes an assessment method that incorporates 3D surface roughness with standard clustering techniques to objectively determine the PASI scaliness score for psoriasis lesions. A surface roughness algorithm using structured light projection has been applied to 1999 3D psoriasis lesion surfaces. The algorithm has been validated with an accuracy of 94.12%. Clustering algorithms were used to classify the surface roughness measured using the proposed assessment method for PASI scaliness scoring. The reliability of the developed PASI scaliness algorithm was high with kappa coefficients>0.84 (almost perfect agreement).
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  20. Computerised image analysis of vitiligo lesion: evaluation using manually defined lesion areas
    Nugroho H, Ahmad Fadzil MH, Shamsudin N, Hussein SH
    Skin Res Technol, 2013 Feb;19(1):e72-7.
    PMID: 22233154 DOI: 10.1111/j.1600-0846.2011.00610.x
    Vitiligo is a cutaneous pigmentary disorder characterized by depigmented macules and patches that result from loss of epidermal melanocytes. Physician evaluates the efficacy of treatment by comparing the extent of vitiligo lesions before and after treatment based on the overall visual impression of the treatment response. This method is called the physician's global assessment (PGA) which is subjective. In this article, we present an innovative digital image processing method to determine vitiligo lesion area in an objective manner.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
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