Displaying publications 41 - 60 of 120 in total

Abstract:
Sort:
  1. Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation
    Peng P, Wu D, Huang LJ, Wang J, Zhang L, Wu Y, et al.
    Interdiscip Sci, 2024 Mar;16(1):39-57.
    PMID: 37486420 DOI: 10.1007/s12539-023-00580-0
    Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  2. 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*
  3. 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
  4. 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*
  5. Computerized segmentation and measurement of chronic wound images
    Ahmad Fauzi MF, Khansa I, Catignani K, Gordillo G, Sen CK, Gurcan MN
    Comput Biol Med, 2015 May;60:74-85.
    PMID: 25756704 DOI: 10.1016/j.compbiomed.2015.02.015
    An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  6. Fulltext Design and development of a content-based medical image retrieval system for spine vertebrae irregularity
    Mustapha A, Hussain A, Samad SA, Zulkifley MA, Diyana Wan Zaki WM, Hamid HA
    Biomed Eng Online, 2015;14:6.
    PMID: 25595511 DOI: 10.1186/1475-925X-14-6
    Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  7. 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*
  8. Fulltext Investigation of CPD and HMDS sample preparation techniques for cervical cells in developing computer-aided screening system based on FE-SEM/EDX
    Jusman Y, Ng SC, Abu Osman NA
    ScientificWorldJournal, 2014;2014:289817.
    PMID: 25610902 DOI: 10.1155/2014/289817
    This paper investigated the effects of critical-point drying (CPD) and hexamethyldisilazane (HMDS) sample preparation techniques for cervical cells on field emission scanning electron microscopy and energy dispersive X-ray (FE-SEM/EDX). We investigated the visualization of cervical cell image and elemental distribution on the cervical cell for two techniques of sample preparation. Using FE-SEM/EDX, the cervical cell images are captured and the cell element compositions are extracted for both sample preparation techniques. Cervical cell image quality, elemental composition, and processing time are considered for comparison of performances. Qualitatively, FE-SEM image based on HMDS preparation technique has better image quality than CPD technique in terms of degree of spread cell on the specimen and morphologic signs of cell deteriorations (i.e., existence of plate and pellet drying artifacts and membrane blebs). Quantitatively, with mapping and line scanning EDX analysis, carbon and oxygen element compositions in HMDS technique were higher than the CPD technique in terms of weight percentages. The HMDS technique has shorter processing time than the CPD technique. The results indicate that FE-SEM imaging, elemental composition, and processing time for sample preparation with the HMDS technique were better than CPD technique for cervical cell preparation technique for developing computer-aided screening system.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  9. Fulltext A chaotic cryptosystem for images based on Henon and Arnold cat map
    Soleymani A, Nordin MJ, Sundararajan E
    ScientificWorldJournal, 2014;2014:536930.
    PMID: 25258724 DOI: 10.1155/2014/536930
    The rapid evolution of imaging and communication technologies has transformed images into a widespread data type. Different types of data, such as personal medical information, official correspondence, or governmental and military documents, are saved and transmitted in the form of images over public networks. Hence, a fast and secure cryptosystem is needed for high-resolution images. In this paper, a novel encryption scheme is presented for securing images based on Arnold cat and Henon chaotic maps. The scheme uses Arnold cat map for bit- and pixel-level permutations on plain and secret images, while Henon map creates secret images and specific parameters for the permutations. Both the encryption and decryption processes are explained, formulated, and graphically presented. The results of security analysis of five different images demonstrate the strength of the proposed cryptosystem against statistical, brute force and differential attacks. The evaluated running time for both encryption and decryption processes guarantee that the cryptosystem can work effectively in real-time applications.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  10. 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*
  11. Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier
    Islam MS, Hannan MA, Basri H, Hussain A, Arebey M
    Waste Manag, 2014 Feb;34(2):281-90.
    PMID: 24238802 DOI: 10.1016/j.wasman.2013.10.030
    The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  12. Signal-to-noise ratio estimation on SEM images using cubic spline interpolation with Savitzky-Golay smoothing
    Sim KS, Kiani MA, Nia ME, Tso CP
    J Microsc, 2014 Jan;253(1):1-11.
    PMID: 24164248 DOI: 10.1111/jmi.12089
    A new technique based on cubic spline interpolation with Savitzky-Golay noise reduction filtering is designed to estimate signal-to-noise ratio of scanning electron microscopy (SEM) images. This approach is found to present better result when compared with two existing techniques: nearest neighbourhood and first-order interpolation. When applied to evaluate the quality of SEM images, noise can be eliminated efficiently with optimal choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  13. 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*
  14. Reflection coefficient detection of simulation models for microwave imaging simulation system
    Chew KM, Sudirman R, Seman N, Yong CY
    Biomed Mater Eng, 2014;24(1):199-207.
    PMID: 24211899 DOI: 10.3233/BME-130800
    The study was conducted based on two objectives as framework. The first objective is to determine the point of microwave signal reflection while penetrating into the simulation models and, the second objective is to analyze the reflection pattern when the signal penetrate into the layers with different relative permittivity, εr. Thus, several microwave models were developed to make a close proximity of the in vivo human brain. The study proposed two different layers on two different characteristics models. The radii on the second layer and the corresponding antenna positions are the factors for both models. The radii for model 1 is 60 mm with an antenna position of 10 mm away, in contrast, model 2 is 10 mm larger in size with a closely adapted antenna without any gap. The layers of the models were developed with different combination of materials such as Oil, Sandy Soil, Brain, Glycerin and Water. Results show the combination of Glycerin + Brain and Brain + Sandy Soil are the best proximity of the in vivo human brain grey and white matter. The results could benefit subsequent studies for further enhancement and development of the models.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  15. 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*
  16. 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*
  17. Fulltext An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA)
    Chai HY, Wee LK, Swee TT, Salleh ShH, Chea LY
    Biomed Eng Online, 2011;10:87.
    PMID: 21952080 DOI: 10.1186/1475-925X-10-87
    Segmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  18. Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
    Ghanizadeh A, Abarghouei AA, Sinaie S, Saad P, Shamsuddin SM
    Appl Opt, 2011 Jul 1;50(19):3191-200.
    PMID: 21743518 DOI: 10.1364/AO.50.003191
    Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  19. 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*
  20. 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
Related Terms
Filters
Contact Us

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

External Links