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Fulltext Investigation of a novel common subexpression elimination method for low power and area efficient DCT architecture

Siddiqui MF, Reza AW, Kanesan J, Ramiah H

ScientificWorldJournal, 2014;2014:620868.
PMID: 25133249 DOI: 10.1155/2014/620868

A wide interest has been observed to find a low power and area efficient hardware design of discrete cosine transform (DCT) algorithm. This research work proposed a novel Common Subexpression Elimination (CSE) based pipelined architecture for DCT, aimed at reproducing the cost metrics of power and area while maintaining high speed and accuracy in DCT applications. The proposed design combines the techniques of Canonical Signed Digit (CSD) representation and CSE to implement the multiplier-less method for fixed constant multiplication of DCT coefficients. Furthermore, symmetry in the DCT coefficient matrix is used with CSE to further decrease the number of arithmetic operations. This architecture needs a single-port memory to feed the inputs instead of multiport memory, which leads to reduction of the hardware cost and area. From the analysis of experimental results and performance comparisons, it is observed that the proposed scheme uses minimum logic utilizing mere 340 slices and 22 adders. Moreover, this design meets the real time constraints of different video/image coders and peak-signal-to-noise-ratio (PSNR) requirements. Furthermore, the proposed technique has significant advantages over recent well-known methods along with accuracy in terms of power reduction, silicon area usage, and maximum operating frequency by 41%, 15%, and 15%, respectively.

Matched MeSH terms: Photography/methods
Fulltext Capability of integrated MODIS imagery and ALOS for oil palm, rubber and forest areas mapping in tropical forest regions

Razali SM, Marin A, Nuruddin AA, Shafri HZ, Hamid HA

Sensors (Basel), 2014 May 07;14(5):8259-82.
PMID: 24811079 DOI: 10.3390/s140508259

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.

Matched MeSH terms: Photography/methods*
Fulltext Projecting range-wide sun bear population trends using tree cover and camera-trap bycatch data

Scotson L, Fredriksson G, Ngoprasert D, Wong WM, Fieberg J

PLoS One, 2017;12(9):e0185336.
PMID: 28961243 DOI: 10.1371/journal.pone.0185336

Monitoring population trends of threatened species requires standardized techniques that can be applied over broad areas and repeated through time. Sun bears Helarctos malayanus are a forest dependent tropical bear found throughout most of Southeast Asia. Previous estimates of global population trends have relied on expert opinion and cannot be systematically replicated. We combined data from 1,463 camera traps within 31 field sites across sun bear range to model the relationship between photo catch rates of sun bears and tree cover. Sun bears were detected in all levels of tree cover above 20%, and the probability of presence was positively associated with the amount of tree cover within a 6-km2 buffer of the camera traps. We used the relationship between catch rates and tree cover across space to infer temporal trends in sun bear abundance in response to tree cover loss at country and global-scales. Our model-based projections based on this "space for time" substitution suggested that sun bear population declines associated with tree cover loss between 2000-2014 in mainland southeast Asia were ~9%, with declines highest in Cambodia and lowest in Myanmar. During the same period, sun bear populations in insular southeast Asia (Malaysia, Indonesia and Brunei) were projected to have declined at a much higher rate (22%). Cast forward over 30-years, from the year 2000, by assuming a constant rate of change in tree cover, we projected population declines in the insular region that surpassed 50%, meeting the IUCN criteria for endangered if sun bears were listed on the population level. Although this approach requires several assumptions, most notably that trends in abundance across space can be used to infer temporal trends, population projections using remotely sensed tree cover data may serve as a useful alternative (or supplement) to expert opinion. The advantages of this approach is that it is objective, data-driven, repeatable, and it requires that all assumptions be clearly stated.

Matched MeSH terms: Photography/methods*
Automated pterygium detection method of anterior segment photographed images

Wan Zaki WMD, Mat Daud M, Abdani SR, Hussain A, Mutalib HA

Comput Methods Programs Biomed, 2018 Feb;154:71-78.
PMID: 29249348 DOI: 10.1016/j.cmpb.2017.10.026

BACKGROUND AND BJECTIVE: Pterygium is an ocular disease caused by fibrovascular tissue encroachment onto the corneal region. The tissue may cause vision blurring if it grows into the pupil region. In this study, we propose an automatic detection method to differentiate pterygium from non-pterygium (normal) cases on the basis of frontal eye photographed images, also known as anterior segment photographed images.
METHODS: The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network.
RESULTS: The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively.
CONCLUSION: A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.

Matched MeSH terms: Photography/methods*
Fulltext Sudden event recognition: a survey

Suriani NS, Hussain A, Zulkifley MA

Sensors (Basel), 2013 Aug 05;13(8):9966-98.
PMID: 23921828 DOI: 10.3390/s130809966

Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

Matched MeSH terms: Photography/methods*
Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G

Lim WX, Chen Z

Med Biol Eng Comput, 2024 Aug;62(8):2571-2583.
PMID: 38649629 DOI: 10.1007/s11517-024-03093-0

Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermore, the computational cost of inspecting each pixel is expensive because the number of pixels is high even at low resolution. In this work, we propose a hybrid image processing method. Simple Linear Iterative Clustering with Gaussian Filter (SLIC-G) for the purpose of overcoming pixel constraints. The SLIC-G image processing method is divided into two stages: (1) simple linear iterative clustering superpixel segmentation and (2) Gaussian smoothing operation. In such a way, a large number of new transformed datasets are generated and then used for model training. Finally, two performance evaluation metrics that are suitable for imbalanced diabetic retinopathy datasets were used to validate the effectiveness of the proposed SLIC-G. The results indicate that, in comparison to prior published works' results, the proposed SLIC-G shows better performance on image classification of class imbalanced diabetic retinopathy datasets. This research reveals the importance of image processing and how it influences the performance of deep learning networks. The proposed SLIC-G enhances pre-trained network performance by eliminating the local redundancy of an image, which preserves local structures, but avoids over-segmented, noisy clips. It closes the research gap by introducing the use of superpixel segmentation and Gaussian smoothing operation as image processing methods in diabetic retinopathy-related tasks.

Matched MeSH terms: Photography/methods
Fulltext Improving RLRN image splicing detection with the Use of PCA and kernel PCA

Moghaddasi Z, Jalab HA, Md Noor R, Aghabozorgi S

ScientificWorldJournal, 2014;2014:606570.
PMID: 25295304 DOI: 10.1155/2014/606570

Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques. Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms. However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features. Moreover, existing algorithms are limited by high computational time. This study focuses on improving one of the image splicing detection algorithms, that is, the run length run number algorithm (RLRN), by applying two dimension reduction methods, namely, principal component analysis (PCA) and kernel PCA. Support vector machine is used to distinguish between authentic and spliced images. Results show that kernel PCA is a nonlinear dimension reduction method that has the best effect on R, G, B, and Y channels and gray-scale images.

Matched MeSH terms: Photography/methods*
Novel techniques for enhancement and segmentation of acne vulgaris lesions

Malik AS, Humayun J, Kamel N, Yap FB

Skin Res Technol, 2014 Aug;20(3):322-31.
PMID: 24329769 DOI: 10.1111/srt.12122

BACKGROUND: More than 99% acne patients suffer from acne vulgaris. While diagnosing the severity of acne vulgaris lesions, dermatologists have observed inter-rater and intra-rater variability in diagnosis results. This is because during assessment, identifying lesion types and their counting is a tedious job for dermatologists. To make the assessment job objective and easier for dermatologists, an automated system based on image processing methods is proposed in this study.
OBJECTIVES: There are two main objectives: (i) to develop an algorithm for the enhancement of various acne vulgaris lesions; and (ii) to develop a method for the segmentation of enhanced acne vulgaris lesions.
METHODS: For the first objective, an algorithm is developed based on the theory of high dynamic range (HDR) images. The proposed algorithm uses local rank transform to generate the HDR images from a single acne image followed by the log transformation. Then, segmentation is performed by clustering the pixels based on Mahalanobis distance of each pixel from spectral models of acne vulgaris lesions.
RESULTS: Two metrics are used to evaluate the enhancement of acne vulgaris lesions, i.e., contrast improvement factor (CIF) and image contrast normalization (ICN). The proposed algorithm is compared with two other methods. The proposed enhancement algorithm shows better result than both the other methods based on CIF and ICN. In addition, sensitivity and specificity are calculated for the segmentation results. The proposed segmentation method shows higher sensitivity and specificity than other methods.
CONCLUSION: This article specifically discusses the contrast enhancement and segmentation for automated diagnosis system of acne vulgaris lesions. The results are promising that can be used for further classification of acne vulgaris lesions for final grading of the lesions.
KEYWORDS: acne grading; acne lesions; acne vulgaris; enhancement; segmentation

Matched MeSH terms: Photography/methods*
Fulltext Acne analysis, grading and computational assessment methods: an overview

Ramli R, Malik AS, Hani AF, Jamil A

Skin Res Technol, 2012 Feb;18(1):1-14.
PMID: 21605170 DOI: 10.1111/j.1600-0846.2011.00542.x

INTRODUCTION: This paper presents a comprehensive review of acne grading and measurement. Acne is a chronic disorder of the pilosebaceous units, with excess sebum production, follicular epidermal hyperproliferation, inflammation and Propionibacterium acnes activity. Most patients are affected with acne vulgaris, which is the prevalent type of acne. Acne vulgaris consists of comedones (whitehead and blackhead), papules, pustules, nodules and cysts.
OBJECTIVES: To review and identify the issues for acne vulgaris grading and computational assessment methods. To determine the future direction for addressing the identified issues.
METHODS: There are two main methods of assessment for acne severity grading, namely, lesion counting and comparison of patient with a photographic standard. For the computational assessment method, the emphasis is on computational imaging techniques.
RESULTS: Current acne grading methods are very time consuming and tedious. Generally, they rely on approximation for counting lesions and hence the assessment is quite subjective, with both inter and intra-observer variability. It is important to accurately assess acne grade to evaluate its severity as this influences treatment selection and assessment of response to therapy. This will further help in better disease management and more efficacious treatment.
CONCLUSION: Semi-automated or automated methods based on computational imaging techniques should be devised for acne grade assessment.

Matched MeSH terms: Photography/methods*
Determination of foveal avascular zone in diabetic retinopathy digital fundus images

Ahmad Fadzil MH, Izhar LI, Nugroho HA

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

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

Matched MeSH terms: Photography/methods*
Comparison of B-scan sonographic measurements of optic cup with fundus photographic measurements

Ali NA, Subrayan V, Reddy SC, Othman F

J Clin Ultrasound, 2009 Jun;37(5):285-9.
PMID: 19280658 DOI: 10.1002/jcu.20570

To compare the measurements of the optic cup diameter with B-scan sonography with fundus photography in patients with clear ocular media and to propose a solution for the clinical problem of determining the cup-disc ratio in eyes with opaque ocular media.

Matched MeSH terms: Photography/methods
Fulltext Photo-ID and telemetry highlight a global whale shark hotspot in Palawan, Philippines

Araujo G, Agustines A, Tracey B, Snow S, Labaja J, Ponzo A

Sci Rep, 2019 11 20;9(1):17209.
PMID: 31748588 DOI: 10.1038/s41598-019-53718-w

The Philippines is home to the second largest known population of whale sharks in the world. The species is listed as endangered due to continued population declines in the Indo-Pacific. Knowledge about the connectivity within Southeast Asia remains poor, and thus international management is difficult. Here, we employed pop-up archival tags, data mining and dedicated effort to understand an aggregation of whale sharks at Honda Bay, Palawan, Philippines, and its role in the species' conservation. Between Apr and Oct 2018, we conducted 159 surveys identifying 117 individual whale sharks through their unique spot patterns (96.5% male, mean 4.5 m). A further 66 individual whale sharks were identified from local operators, and data mined on social media platforms. The satellite telemetry data showed that the whale sharks moved broadly, with one individual moving to Sabah, Malaysia, before returning to the site <1 year later. Similarly, another tagged whale shark returned to the site at a similar periodicity after reaching the Malay-Filipino border. One individual whale shark first identified in East Kalimantan, Indonesia by a citizen scientist was resighted in Honda Bay ~3.5 years later. Honda Bay is a globally important site for the endangered whale shark with connectivity to two neighbouring countries, highlighting the need for international cooperation to manage the species.

Matched MeSH terms: Photography/methods
Fulltext Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing

Leong SH, Ong SH

PLoS One, 2017;12(7):e0180307.
PMID: 28686634 DOI: 10.1371/journal.pone.0180307

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index.

Matched MeSH terms: Photography/methods
Corneal thickness and volume in subclinical and clinical keratoconus

Ahmadi Hosseini SM, Mohidin N, Abolbashari F, Mohd-Ali B, Santhirathelagan CT

Int Ophthalmol, 2013 Apr;33(2):139-45.
PMID: 23138667 DOI: 10.1007/s10792-012-9654-x

To evaluate corneal thickness and volume in subclinical and clinical keratoconus in Asian population with the aim of discriminating between normal and ectatic cornea. Eyes were placed into one of the following three groups: normal, subclinical, and mild-moderate keratoconus. Pentacam Scheimpflug imaging (Oculus Inc., Wetzlar, Germany) was performed for each participant to record thinnest corneal thickness, central corneal thickness, corneal volume (CV), peripheral corneal thickness (PCT) and percentage thickness increase (PTI) at 2, 4, 6, and 8 mm. The data were exported to SPSS for statistical analysis. Subjects comprised 52 normal, 15 subclinical keratoconus, and 32 mild-moderate clinical keratoconus eyes. Our results indicated that corneal thickness (CT) distribution, PTI, and CV in normal eyes were significantly different compared with subclinical and clinical keratoconus (P < .05). Overall, subclinical group exhibited lower CT distribution and volume, and higher PTI in comparison with normal eyes. However, they showed higher CT distribution and volume, and lower PTI compared with keratoconus group. In addition, there was a smaller change in PCT and PTI from the thinnest point of the cornea to the periphery. The results of the present study indicate that CT parameters and CV were significantly different in normal versus subclinical group and in normal versus keratoconus group. These findings could help clinicians to better discriminate between normal and ectatic cornea.

Matched MeSH terms: Photography/methods
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: Photography/methods*
Fulltext Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm-Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy

Bawankar P, Shanbhag N, K SS, Dhawan B, Palsule A, Kumar D, et al.

PLoS One, 2017;12(12):e0189854.
PMID: 29281690 DOI: 10.1371/journal.pone.0189854

Diabetic retinopathy (DR) is a leading cause of blindness among working-age adults. Early diagnosis through effective screening programs is likely to improve vision outcomes. The ETDRS seven-standard-field 35-mm stereoscopic color retinal imaging (ETDRS) of the dilated eye is elaborate and requires mydriasis, and is unsuitable for screening. We evaluated an image analysis application for the automated diagnosis of DR from non-mydriatic single-field images. Patients suffering from diabetes for at least 5 years were included if they were 18 years or older. Patients already diagnosed with DR were excluded. Physiologic mydriasis was achieved by placing the subjects in a dark room. Images were captured using a Bosch Mobile Eye Care fundus camera. The images were analyzed by the Retinal Imaging Bosch DR Algorithm for the diagnosis of DR. All subjects also subsequently underwent pharmacological mydriasis and ETDRS imaging. Non-mydriatic and mydriatic images were read by ophthalmologists. The ETDRS readings were used as the gold standard for calculating the sensitivity and specificity for the software. 564 consecutive subjects (1128 eyes) were recruited from six centers in India. Each subject was evaluated at a single outpatient visit. Forty-four of 1128 images (3.9%) could not be read by the algorithm, and were categorized as inconclusive. In four subjects, neither eye provided an acceptable image: these four subjects were excluded from the analysis. This left 560 subjects for analysis (1084 eyes). The algorithm correctly diagnosed 531 of 560 cases. The sensitivity, specificity, and positive and negative predictive values were 91%, 97%, 94%, and 95% respectively. The Bosch DR Algorithm shows favorable sensitivity and specificity in diagnosing DR from non-mydriatic images, and can greatly simplify screening for DR. This also has major implications for telemedicine in the use of screening for retinopathy in patients with diabetes mellitus.

Study site: India

Matched MeSH terms: Photography/methods*

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