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A comparative study of computed radiography-based mammography using digital phosphor storage plate and full field digital mammography

Chelliah KK, Tamanang S, Bt Elias LS, Ying KY

Indian J Med Sci, 2013 11 2;67(1-2):23-8.
PMID: 24178338

BACKGROUND: Two digital mammography systems, based on different physical concepts, have been introduced in the last few years namely the full-field digital mammography (FFDM) system and computed radiography-based mammography using digital storage phosphor plate (DSPM).
AIMS: The objective of this study was to compare the image quality for DSPM and FFDM using a grading scale based on previously published articles.
MATERIALS AND METHODS: This comparative diagnostic study was done for 5-month duration at the Breast Clinic. The system used was the Lorad Selenia FFDM system and the Mammomat 3000 Nova DSPM system. The craniocaudal and mediolateral oblique projections were done on both breast on 58 asymptomatic women using both DSPM and FFDM. The mammograms were evaluated for eight criteria of image quality: Tissue coverage, compression, exposure, contrast, resolution, noise, artifact, and sharpness by two independent radiologists.
STATISTICAL ANALYSIS: Wilcoxon Signed Rank Test and Weighted Kappa.
RESULTS: FFDM was rated significantly better (P < 0.05) for five aspects: Tissue coverage, compression, contrast, exposure, and resolution and equal to DSPM for sharpness, noise, and artifact.
CONCLUSION: FFDM was superior in five aspects and equal to DSPM for three aspects of image quality.

Matched MeSH terms: Radiographic Image Enhancement/methods*
A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram

Meselhy Eltoukhy M, Faye I, Belhaouari Samir B

Comput Biol Med, 2010 Apr;40(4):384-91.
PMID: 20163793 DOI: 10.1016/j.compbiomed.2010.02.002

This paper presents a comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram. Using multiresolution analysis, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands. A set of the biggest coefficients from each decomposition level is extracted. Then a supervised classifier system based on Euclidian distance is constructed. The performance of the classifier is evaluated using a 2 x 5-fold cross validation followed by a statistical analysis. The experimental results suggest that curvelet transform outperforms wavelet transform and the difference is statistically significant.

Matched MeSH terms: Image Enhancement/methods*
A fuzzy-based texture analysis for tissue characterization of diffused liver diseases on B-scan images

Gangeh MJ, Hanmandlu M, Bister M

Biomed Sci Instrum, 2002;38:369-74.
PMID: 12085634

The specific texture on B-scan images is believed to be related to both ultrasound machine characteristics and tissue properties, i.e., the pathological states of the soft tissue. Therefore, for classification, features can be extracted with the use of image texture analysis techniques. In this paper a novel fuzzy approach for texture characterization is used for classification of normal liver and diffused liver diseases, here fatty liver, liver cirrhosis, and hepatitis are emphasized. The texture analysis techniques are diversified by the existence of several approaches. We propose fuzzy features for the analysis of the texture image. For this, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors: maximum, entropy, and energy as used in co-occurrence method, for each window.

Matched MeSH terms: Image Enhancement/methods*
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 Enhancement/methods
Fulltext A new blood vessel extraction technique using edge enhancement and object classification

Badsha S, Reza AW, Tan KG, Dimyati K

J Digit Imaging, 2013 Dec;26(6):1107-15.
PMID: 23515843 DOI: 10.1007/s10278-013-9585-8

Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.

Matched MeSH terms: Image Enhancement/methods*
A robust background correction algorithm for forensic bloodstain imaging using mean-based contrast adjustment

Lee WC, Khoo BE, Abdullah AFL

Sci Justice, 2016 May;56(3):201-209.
PMID: 27162018 DOI: 10.1016/j.scijus.2016.01.001

Background correction algorithm (BCA) is useful in enhancing the visibility of images captured in crime scenes especially those of untreated bloodstains. Successful implementation of BCA requires all the images to have similar brightness which often proves a problem when using automatic exposure setting in a camera. This paper presents an improved background correction algorithm (BCA) that applies mean-based contrast adjustment as a pre-correction step to adjust the mean brightness of images to be similar before implementing BCA. The proposed modification, namely mean-based adaptive BCA (mABCA) was tested on various image samples captured under different illuminations such as 385 nm, 415 nm and 458 nm. We also evaluated mABCA of two wavelengths (415 nm and 458 nm) and three wavelengths (415 nm, 380 nm and 458 nm) in enhancing untreated bloodstains on different surfaces. The proposed mABCA is found to be more robust in processing images captured in different brightness and thus overcomes the main issue faced in the original BCA.

Matched MeSH terms: Image Enhancement/methods*
Fulltext An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection

Saleh MD, Eswaran C, Mueen A

J Digit Imaging, 2011 Aug;24(4):564-72.
PMID: 20524139 DOI: 10.1007/s10278-010-9302-9

This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

Matched MeSH terms: Image Enhancement/methods*
An efficient method for the computation of Legendre moments

Yap PT, Paramesran R

IEEE Trans Pattern Anal Mach Intell, 2005 Dec;27(12):1996-2002.
PMID: 16355666

Legendre moments are continuous moments, hence, when applied to discrete-space images, numerical approximation is involved and error occurs. This paper proposes a method to compute the exact values of the moments by mathematically integrating the Legendre polynomials over the corresponding intervals of the image pixels. Experimental results show that the values obtained match those calculated theoretically, and the image reconstructed from these moments have lower error than that of the conventional methods for the same order. Although the same set of exact Legendre moments can be obtained indirectly from the set of geometric moments, the computation time taken is much longer than the proposed method.

Matched MeSH terms: Image Enhancement/methods*
Fulltext An efficient post-processing adaptive filtering technique to rectifying the flickering effects

Gandam A, Sidhu JS, Verma S, Jhanjhi NZ, Nayyar A, Abouhawwash M, et al.

PLoS One, 2021;16(5):e0250959.
PMID: 33970949 DOI: 10.1371/journal.pone.0250959

Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7-1.9db) while (35.83-47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.

Matched MeSH terms: Image Enhancement/methods*
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 Enhancement/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: Image Enhancement/methods*
Benefits of 3D rotational DSA compared with 2D DSA in the evaluation of intracranial aneurysm

Wong SC, Nawawi O, Ramli N, Abd Kadir KA

Acad Radiol, 2012 Jun;19(6):701-7.
PMID: 22578227 DOI: 10.1016/j.acra.2012.02.012

The aim of this study was to compare conventional two-dimensional (2D) digital subtraction angiography (DSA) with three-dimensional (3D) rotational DSA in the investigation of intracranial aneurysm in terms of detection, size measurement, neck diameter, neck delineation, and relationship with surrounding vessels. A further aim was to compare radiation dose, contrast volume, and procedural time between the two protocols.

Matched MeSH terms: Radiographic Image Enhancement/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 Enhancement/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 Enhancement/methods*
Breast cancer diagnosis in digital mammogram using multiscale curvelet transform

Eltoukhy MM, Faye I, Samir BB

Comput Med Imaging Graph, 2010 Jun;34(4):269-76.
PMID: 20004076 DOI: 10.1016/j.compmedimag.2009.11.002

This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms.

Matched MeSH terms: Radiographic Image Enhancement/methods*
Comparison study of the MRI shoulder PROPELLER technique with differential image quality by variation of shoulder coils

Khairi M, Zakaria F, Supar R, Mohd Z

Med J Malaysia, 2024 Mar;79(Suppl 1):74-81.
PMID: 38555889

INTRODUCTION: Motion and pulsation artifacts are the most prominent types of artifacts in Magnetic Resonance Imaging (MRI) of the shoulder. Therefore, this study examined the Periodically Rotating Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) technique with small flex coil (SFC) and dedicated shoulder coil (DSC) for the reduction of motion and pulsation artifacts. The signalto- noise ratio (SNR) and contrast-to-noise ratio (CNR) of the standard proton density fat saturation (PDFS) pulse sequence and the PROPELLER proton density fat saturation (PROPELLER PDFS) pulse sequence were also evaluated.
MATERIALS AND METHODS: Eighteen (18) participants who met the inclusion and exclusion criteria were scanned using a standard non-contrast MRI shoulder protocol including the PDFS pulse sequence and the PROPELLER PDFS pulse sequence using a small flex coil and a dedicated shoulder coil. Two experienced musculoskeletal (MSK) radiologists evaluated and graded the presence of artifacts on the MR images and the SNR and CNR were measured quantitatively.
RESULTS: The non-parametric Wilcoxon Signed Rank test revealed a significant reduction in motion and pulsation artifacts between the PROPELLER PDFS pulse sequence and the standard PDFS pulse sequence. In addition, the nonparametric Mann-Whitney U test revealed that the mean rank of SNR for the standard sequence was statistically significant when compared to the PROPELLER sequence for both coil types. The CNR of the PROPELLER sequence was statistically significant between fat-fluid, bone-fluid, bonetendon, bone-muscle, and muscle-fluid when using SFC and DSC.
CONCLUSION: This study proved that the PROPELLER-PDFS pulse sequence effectively eliminates motion and pulsation artifacts, regardless of the coils utilised. The PROPELLERPDFS pulse sequence can therefore be implemented into the standard MRI shoulder procedure.

Matched MeSH terms: Image Enhancement/methods
Fulltext Completed local ternary pattern for rotation invariant texture classification

Rassem TH, Khoo BE

ScientificWorldJournal, 2014;2014:373254.
PMID: 24977193 DOI: 10.1155/2014/373254

Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter's weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.

Matched MeSH terms: Image Enhancement/methods*
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review

Jalalian A, Mashohor SB, Mahmud HR, Saripan MI, Ramli AR, Karasfi B

Clin Imaging, 2013 May-Jun;37(3):420-6.
PMID: 23153689 DOI: 10.1016/j.clinimag.2012.09.024

Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.

Matched MeSH terms: Image Enhancement/methods*
Cost-effective handling of digital medical images in the telemedicine environment

Choong MK, Logeswaran R, Bister M

Int J Med Inform, 2007 Sep;76(9):646-54.
PMID: 16769242

This paper concentrates on strategies for less costly handling of medical images. Aspects of digitization using conventional digital cameras, lossy compression with good diagnostic quality, and visualization through less costly monitors are discussed.

Matched MeSH terms: Radiographic Image Enhancement/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 Enhancement/methods*

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