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Image signal-to-noise ratio estimation using Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average model

Sim KS, Wee MY, Lim WK

Microsc Res Tech, 2008 Oct;71(10):710-20.
PMID: 18615490 DOI: 10.1002/jemt.20610

We propose to cascade the Shape-Preserving Piecewise Cubic Hermite model with the Autoregressive Moving Average (ARMA) interpolator; we call this technique the Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average (SP2CHARMA) model. In a few test cases involving different images, this model is found to deliver an optimum solution for signal to noise ratio (SNR) estimation problems under different noise environments. The performance of the proposed estimator is compared with two existing methods: the autoregressive-based and autoregressive moving average estimators. Being more robust with noise, the SP2CHARMA estimator has efficiency that is significantly greater than those of the two methods.

Matched MeSH terms: Signal-To-Noise Ratio
Correlation between subjective and objective assessment of magnetic resonance (MR) images

Chow LS, Rajagopal H, Paramesran R, Alzheimer's Disease Neuroimaging Initiative

Magn Reson Imaging, 2016 07;34(6):820-831.
PMID: 26969762 DOI: 10.1016/j.mri.2016.03.006

Medical Image Quality Assessment (IQA) plays an important role in assisting and evaluating the development of any new hardware, imaging sequences, pre-processing or post-processing algorithms. We have performed a quantitative analysis of the correlation between subjective and objective Full Reference - IQA (FR-IQA) on Magnetic Resonance (MR) images of the human brain, spine, knee and abdomen. We have created a MR image database that consists of 25 original reference images and 750 distorted images. The reference images were distorted with six types of distortions: Rician Noise, Gaussian White Noise, Gaussian Blur, DCT compression, JPEG compression and JPEG2000 compression, at various levels of distortion. Twenty eight subjects were chosen to evaluate the images resulting in a total of 21,700 human evaluations. The raw scores were then converted to Difference Mean Opinion Score (DMOS). Thirteen objective FR-IQA metrics were used to determine the validity of the subjective DMOS. The results indicate a high correlation between the subjective and objective assessment of the MR images. The Noise Quality Measurement (NQM) has the highest correlation with DMOS, where the mean Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are 0.936 and 0.938 respectively. The Universal Quality Index (UQI) has the lowest correlation with DMOS, where the mean PLCC and SROCC are 0.807 and 0.815 respectively. Student's T-test was used to find the difference in performance of FR-IQA across different types of distortion. The superior IQAs tested statistically are UQI for Rician noise images, Visual Information Fidelity (VIF) for Gaussian blur images, NQM for both DCT and JPEG compressed images, Peak Signal-to-Noise Ratio (PSNR) for JPEG2000 compressed images.

Matched MeSH terms: Signal-To-Noise Ratio
Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

Khairur Rijal Jamaludin, Nolia Harudin, Faizir Ramlie, Mohd Nabil Muhtazaruddin, Che Munira Che Razali, Wan Zuki Azman Wan Muhamad

MATEMATIKA, 2020;36(1):69-84.
MyJurnal

Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of determination (R2) value for the model creation, the optimization process in reducing the number of variables would be much reliable and accurate. Comparing between T-Method+OA and T-Method+BPSO in four different case study, it shows that T-Method+BPSO performing better with greater R2 and means relative error (MRE) value compared to T-Method+OA.

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext Image quality of coronary CT angiography (CCTA) using 640-slice scanner: qualitative and quantitative assessments of coronary arteries visibility

Shahril Shamsul, Akmal Sabarudin, Hamzaini Abdul Hamid, Norzailin Abu Bakar, Oteh Maskon, Muhammad Khalis Abdul Karim

Jurnal Sains Kesihatan Malaysia, 2020;18(2):49-57.
MyJurnal

The purpose of this study was to evaluate the image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA) using 640-slice scanner. Advancement of multidetector computed tomography (MDCT) technology with higher spatial, temporal resolution, and increasing detector array have improved the image quality and diagnostic accuracy of CCTA. A total of 25 patients (12 men and 13 women) underwent CCTA examination was chosen and data was acquired by 640-slice scanner. All 16 segments of coronary arteries were evaluated by two reviewers using a 4-likert scale for qualitative assessment. In quantitative assessment, the evaluation of 4 main coronary arteries were analysed in terms of signal intensity (SI), image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). All 25 patients with a mean age of 52.88 ± 14.75 years old and body mass index (BMI) of 24.24 ± 3.28 kg/m2 were analysed. In qualitative assessment, from the total of 400 segments, 379 segments (95%) had diagnostic value while 21 segments did not have diagnostic value, which means 5% artefact was detected. In quantitative assessment, there was no statistical differences in gender, race, and BMI (p>0.05). Overall evaluation showed that higher SI at the left main artery (LM) at 393.7 ± 47.19. Image noise was higher at right coronary artery (RCA) at 39.01 ± 13.97. SNR and CNR showed higher at left anterior descending (LAD) with 12.73 ± 5.17 and LM 9.14 ± 4.2, respectively. In conclusion, this study indicates that 640-slice MDCT has higher diagnostic value in CCTA examination with 95% vessel visibility with 5% artefact detection.

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext Linguistic context effects in the speech perfection of Malay-speaking normal hearing young adults

Cila Umat, Nahazatul Islia Jamari

Jurnal Sains Kesihatan Malaysia, 2006;4(1):9-20.
MyJurnal

The study examined the use of linguistic contextual cues among native, Malay-speaking normal hearing young adults. Ten undergraduate students of Universiti Kebangsaan Malaysia participated in the study. All subjects had normal hearing with the average hearing threshold levels for the overall left and the right ears of 7.8 dB (SD 4.1). The Malay Hearing in Noise Test (MyHINT) materials were employed and presented to the subjects at an approximately 65 dBA presentation level. Testing was conducted in a sound field in three different listening conditions: in quiet, in noise with +5 dB signal-to-noise ratio (SNR) and 0 dB SNR. In every test condition, three lists of MyHINT were administered to each subject. The magnitude of context effects was measured using the j factor, which was derived from measurements of recognition probabilities for whole sentences (13,) and the constituent words in the sentences (PP) in which j = log P./ log P P. Results showed that all subjects scored 100% identification of words in sentences and whole sentences in quiet listening condition, while subjects' performances in 0 dB SNR were significantly poorer than that in quiet and in +5 dB SNR (p < 0.001). The j-values were significantly correlated with the probability of recognizing words in the sentences (r = 0.515, p = 0.029) in which lower j values were associated with lower P ps. Subjects were not significantly different from each other in their use of contextual cues in adverse listening conditions [F(9, 7) = 1.34, p = 0.359]. Using the linear regression function for j on word recognition probabilities, the predicted P. were calculated. It was found that the predicted and measured probabilities of recognizing whole sentences were highly correlated: r = 0.973, p < 0.001. The results suggested that linguistic contextual information become increasingly important for recognition of sentences by normal hearing young adult listeners as SNR deteriorates.

Matched MeSH terms: Signal-To-Noise Ratio
Textitis as Seen on 18F-NaF Imaging Using an Ultra-High-Resolution Positron Emission Mammography Scanner

Usmani S, Rasheed R, Al Kandari F

J Nucl Med Technol, 2020 Jun;48(2):181-183.
PMID: 32111663 DOI: 10.2967/jnmt.119.235986

Textitis is a new term used to refer to the degenerative-strain osteoarthritis that comes from excessive use of a smart phone. 18F-NaF is increasingly used in diagnosing skeletal pain that is not identified on radiographs. We report a case of a 26-y-old woman with left breast cancer referred for 18F-NaF PET/CT, who was complaining of right thumb and wrist pain. Findings were negative for bone secondaries. Dedicated hands views were acquired on a positron emission mammography scanner and showed focal uptake at the first carpometacarpal and second metacarpophalangeal joints. On the basis of the strong history, the findings were likely due to active arthritic changes caused by repetitive strain injury from excessive text messaging.

Matched MeSH terms: Signal-To-Noise Ratio*
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: Signal-To-Noise Ratio*
Computer assisted diagnostic system in tumor radiography

Faisal A, Parveen S, Badsha S, Sarwar H, Reza AW

J Med Syst, 2013 Jun;37(3):9938.
PMID: 23504472 DOI: 10.1007/s10916-013-9938-3

An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext Evaluation of an integrated 3D-printed phantom for coronary CT angiography using iterative reconstruction algorithm

Abdullah KA, McEntee MF, Reed W, Kench PL

J Med Radiat Sci, 2020 Sep;67(3):170-176.
PMID: 32219989 DOI: 10.1002/jmrs.387

INTRODUCTION: 3D-printed imaging phantoms are now increasingly available and used for computed tomography (CT) dose optimisation study and image quality analysis. The aim of this study was to evaluate the integrated 3D-printed cardiac insert phantom when evaluating iterative reconstruction (IR) algorithm in coronary CT angiography (CCTA) protocols.
METHODS: The 3D-printed cardiac insert phantom was positioned into a chest phantom and scanned with a 16-slice CT scanner. Acquisitions were performed with CCTA protocols using 120 kVp at four different tube currents, 300, 200, 100 and 50 mA (protocols A, B, C and D, respectively). The image data sets were reconstructed with a filtered back projection (FBP) and three different IR algorithm strengths. The image quality metrics of image noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR) were calculated for each protocol.
RESULTS: Decrease in dose levels has significantly increased the image noise, compared to FBP of protocol A (P

Matched MeSH terms: Signal-To-Noise Ratio
Investigating the performance of an amplitude-independent algorithm for detecting the hand muscle activity of stroke survivors

Hameed HK, Wan Hasan WZ, Shafie S, Ahmad SA, Jaafar H, Inche Mat LN

J Med Eng Technol, 2020 Apr;44(3):139-148.
PMID: 32396756 DOI: 10.1080/03091902.2020.1753838

To make robotic hand devices controlled by surface electromyography (sEMG) signals feasible and practical tools for assisting patients with hand impairments, the problems that prevent these devices from being widely used have to be overcome. The most significant problem is the involuntary amplitude variation of the sEMG signals due to the movement of electrodes during forearm motion. Moreover, for patients who have had a stroke or another neurological disease, the muscle activity of the impaired hand is weak and has a low signal-to-noise ratio (SNR). Thus, muscle activity detection methods intended for controlling robotic hand devices should not depend mainly on the amplitude characteristics of the sEMG signal in the detection process, and they need to be more reliable for sEMG signals that have a low SNR. Since amplitude-independent muscle activity detection methods meet these requirements, this paper investigates the performance of such a method on people who have had a stroke in terms of the detection of weak muscle activity and resistance to false alarms caused by the involuntary amplitude variation of sEMG signals; these two parameters are very important for achieving the reliable control of robotic hand devices intended for people with disabilities. A comparison between the performance of an amplitude-independent muscle activity detection algorithm and three amplitude-dependent algorithms was conducted by using sEMG signals recorded from six hemiparesis stroke survivors and from six healthy subjects. The results showed that the amplitude-independent algorithm performed better in terms of detecting weak muscle activity and resisting false alarms.

Matched MeSH terms: Signal-To-Noise Ratio
Analysis of model-based and model-free CEST effect quantification methods for different medical applications

Foo LS, Yap WS, Hum YC, Manan HA, Tee YK

J Magn Reson, 2020 01;310:106648.
PMID: 31760147 DOI: 10.1016/j.jmr.2019.106648

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) holds great potential to provide new metabolic information for clinical applications such as tumor, stroke and Parkinson's Disease diagnosis. Many active research and developments have been conducted to translate this emerging MRI technique for routine clinical applications. In general, there are two CEST quantification techniques: (i) model-free and (ii) model-based techniques. The reliability of these quantification techniques depends heavily on the experimental conditions and quality of the collected data. Errors such as noise may lead to misleading quantification results and thus inaccurate diagnosis when CEST imaging becomes a standard or routine imaging scan in the future. This paper investigates the accuracy and robustness of these quantification techniques under different signal-to-noise (SNR) levels and magnetic field strengths. The quantified CEST effect before and after adding random Gaussian White Noise using model-free and model-based quantification techniques were compared. It was found that the model-free technique consistently yielded larger average percentage error across all tested parameters compared to its model-based counterpart, and that the model-based technique could withstand SNR of about 3 times lower than the model-free technique. When applied on noisy brain tumor, ischemic stroke, and Parkinson's Disease clinical data, the model-free technique failed to produce significant differences between normal and abnormal tissue whereas the model-based technique consistently generated significant differences. Although the model-free technique was less accurate and robust, its simplicity and thus speed would still make it a good approximate when the SNR was high (>50) or when the CEST effect was large and well-defined. For more accurate CEST quantification, model-based techniques should be considered. When SNR was low (<50) and the CEST effect was small such as those acquired from clinical field strength scanners, which are generally 3T and below, model-based techniques should be considered over model-free counterpart to maintain an average percentage error of less than 44% even under very noisy condition as tested in this work.

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext InNetGAN: Inception Network-Based Generative Adversarial Network for Denoising Low-Dose Computed Tomography

Kulathilake KASH, Abdullah NA, Bandara AMRR, Lai KW

J Healthc Eng, 2021;2021:9975762.
PMID: 34552709 DOI: 10.1155/2021/9975762

Low-dose Computed Tomography (LDCT) has gained a great deal of attention in clinical procedures due to its ability to reduce the patient's risk of exposure to the X-ray radiation. However, reducing the X-ray dose increases the quantum noise and artifacts in the acquired LDCT images. As a result, it produces visually low-quality LDCT images that adversely affect the disease diagnosing and treatment planning in clinical procedures. Deep Learning (DL) has recently become the cutting-edge technology of LDCT denoising due to its high performance and data-driven execution compared to conventional denoising approaches. Although the DL-based models perform fairly well in LDCT noise reduction, some noise components are still retained in denoised LDCT images. One reason for this noise retention is the direct transmission of feature maps through the skip connections of contraction and extraction path-based DL modes. Therefore, in this study, we propose a Generative Adversarial Network with Inception network modules (InNetGAN) as a solution for filtering the noise transmission through skip connections and preserving the texture and fine structure of LDCT images. The proposed Generator is modeled based on the U-net architecture. The skip connections in the U-net architecture are modified with three different inception network modules to filter out the noise in the feature maps passing over them. The quantitative and qualitative experimental results have shown the performance of the InNetGAN model in reducing noise and preserving the subtle structures and texture details in LDCT images compared to the other state-of-the-art denoising algorithms.

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext Tamper localization and lossless recovery watermarking scheme with ROI segmentation and multilevel authentication

Liew SC, Liew SW, Zain JM

J Digit Imaging, 2013 Apr;26(2):316-25.
PMID: 22555905 DOI: 10.1007/s10278-012-9484-4

Tamper localization and recovery watermarking scheme can be used to detect manipulation and recover tampered images. In this paper, a tamper localization and lossless recovery scheme that used region of interest (ROI) segmentation and multilevel authentication was proposed. The watermarked images had a high average peak signal-to-noise ratio of 48.7 dB and the results showed that tampering was successfully localized and tampered area was exactly recovered. The usage of ROI segmentation and multilevel authentication had significantly reduced the time taken by approximately 50 % for the tamper localization and recovery processing.

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext Region of Interest-Based Tamper Detection and Lossless Recovery Watermarking Scheme (ROI-DR) on Ultrasound Medical Images

Khor HL, Liew SC, Zain JM

J Digit Imaging, 2017 Jun;30(3):328-349.
PMID: 28050716 DOI: 10.1007/s10278-016-9930-9

Tampering on medical image will lead to wrong diagnosis and treatment, which is life-threatening; therefore, digital watermarking on medical image was introduced to protect medical image from tampering. Medical images are divided into region of interest (ROI) and region of non-interest (RONI). ROI is an area that has a significant impact on diagnosis, whereas RONI has less or no significance in diagnosis. This paper has proposed ROI-based tamper detection and recovery watermarking scheme (ROI-DR) that embeds ROI bit information into RONI least significant bits, which will be extracted later for authentication and recovery process. The experiment result has shown that the ROI-DR has achieved a good result in imperceptibility with peak signal-to-noise ratio (PSNR) values approximately 48 dB, it is robust against various kinds of tampering, and the tampered ROI was able to recover to its original form. Lastly, a comparative table with the previous research (TALLOR and TALLOR-RS watermarking schemes) has been derived, where these three watermarking schemes were tested under the same testing conditions and environment. The experiment result has shown that ROI-DR has achieved speed-up factors of 22.55 and 26.65 in relative to TALLOR and TALLOR-RS watermarking schemes, respectively.

Matched MeSH terms: Signal-To-Noise Ratio
Simultaneous analysis of silicon and boron dissolved in water by combination of electrodialytic salt removal and ion-exclusion chromatography with corona charged aerosol detection

Mori M, Sagara K, Arai K, Nakatani N, Ohira S, Toda K, et al.

J Chromatogr A, 2016 Jan 29;1431:131-7.
PMID: 26755416 DOI: 10.1016/j.chroma.2015.12.064

Selective separation and sensitive detection of dissolved silicon and boron (DSi and DB) in aqueous solution was achieved by combining an electrodialytic ion isolation device (EID) as a salt remover, an ion-exclusion chromatography (IEC) column, and a corona charged aerosol detector (CCAD) in sequence. DSi and DB were separated by IEC on the H(+)-form of a cation exchange resin column using pure water eluent. DSi and DB were detected after IEC separation by the CCAD with much greater sensitivity than by conductimetric detection. The five-channel EID, which consisted of anion and cation acceptors, cathode and anode isolators, and a sample channel, removed salt from the sample prior to the IEC-CCAD. DSi and DB were scarcely attracted to the anion accepter in the EID and passed almost quantitatively through the sample channel. Thus, the coupled EID-IEC-CCAD device can isolate DSi and DB from artificial seawater and hot spring water by efficiently removing high concentrations of Cl(-) and SO4(2-) (e.g., 98% and 80% at 0.10molL(-1) each, respectively). The detection limits at a signal-to-noise ratio of 3 were 0.52μmolL(-1) for DSi and 7.1μmolL(-1) for DB. The relative standard deviations (RSD, n=5) of peak areas were 0.12% for DSi and 4.3% for DB.

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext Increasing iterative reconstruction strength at low tube voltage in coronary CT angiography protocols using 3D-printed and Catphan® 500 phantoms

Abdullah KA, McEntee MF, Reed WM, Kench PL

J Appl Clin Med Phys, 2020 Sep;21(9):209-214.
PMID: 32657493 DOI: 10.1002/acm2.12977

PURPOSE: The purpose of this study was to investigate the effect of increasing iterative reconstruction (IR) algorithm strength at different tube voltages in coronary computed tomography angiography (CCTA) protocols using a three-dimensional (3D)-printed and Catphan® 500 phantoms.
METHODS: A 3D-printed cardiac insert and Catphan 500 phantoms were scanned using CCTA protocols at 120 and 100 kVp tube voltages. All CT acquisitions were reconstructed using filtered back projection (FBP) and Adaptive Statistical Iterative Reconstruction (ASIR) algorithm at 40% and 60% strengths. Image quality characteristics such as image noise, signal-noise ratio (SNR), contrast-noise ratio (CNR), high spatial resolution, and low contrast resolution were analyzed.
RESULTS: There was no significant difference (P > 0.05) between 120 and 100 kVp measures for image noise for FBP vs ASIR 60% (16.6 ± 3.8 vs 16.7 ± 4.8), SNR of ASIR 40% vs ASIR 60% (27.3 ± 5.4 vs 26.4 ± 4.8), and CNR of FBP vs ASIR 40% (31.3 ± 3.9 vs 30.1 ± 4.3), respectively. Based on the Modulation Transfer Function (MTF) analysis, there was a minimal change of image quality for each tube voltage but increases when higher strengths of ASIR were used. The best measure of low contrast detectability was observed at ASIR 60% at 120 kVp.
CONCLUSIONS: Changing the IR strength has yielded different image quality noise characteristics. In this study, the use of 100 kVp and ASIR 60% yielded comparable image quality noise characteristics to the standard CCTA protocols using 120 kVp of ASIR 40%. A combination of 3D-printed and Catphan® 500 phantoms could be used to perform CT dose optimization protocols.

Matched MeSH terms: Signal-To-Noise Ratio
Effect of iterative reconstruction algorithm levels on noise index and figure-of-merit in CT pulmonary angiography examinations

Harun HH, Karim MKA, Abbas Z, Sabarudin A, Muniandy SC, Ibahim MJ

J Xray Sci Technol, 2020;28(5):893-903.
PMID: 32741801 DOI: 10.3233/XST-200699

PURPOSE: To evaluate the influence of iterative reconstruction (IR) levels on Computed Tomography (CT) image quality and to establish Figure of Merit (FOM) value for CT Pulmonary Angiography (CTPA) examinations.
METHODS: Images of 31 adult patients who underwent CTPA examinations in our institution from March to April 2019 were retrospectively collected. Other data, such as scanning parameters, radiation dose and body habitus information from the subjects were also recorded. Six different levels of IR were applied to the volume data of the subjects. Five circles of the region of interest (ROI) were drawn in five different arteries namely, pulmonary trunk, right pulmonary artery, left pulmonary artery, ascending aorta and descending aorta. The mean Signal-to-noise ratio (SNR) was obtained, and the FOM was calculated in a fraction of the SNR2 divided by volume-weighted CT dose index (CTDIvol) and SNR2 divided by the size-specific dose estimates (SSDE).
RESULTS: Overall, we observed that the mean value of CTDIvol and SSDE were 13.79±7.72 mGy and 17.25±8.92 mGy, respectively. Notably, SNR values significantly increase with increase of the IR level (p

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext Morphological techniques for microcalcification and mass enhancement

Nor'aida Khairuddin, Norriza Mohd Isa, Wan Muhamad Saridan Wan Hassan

Journal of Nuclear and Related Technologies, 2012;2012(2):102-108.
MyJurnal

The recognition of microcalcifications and masses from digital mammographic images are important to aid the detection of breast cancer. In this paper, we applied morphological techniques to extract the embedded structures from the images for subsequent analysis. A mammographic phantom was created with embedded structures such as micronodules, nodules and fibrils. For the preprocessing techniques, intensity transformation of gray scale was applied to the image. The structures of the image were enhanced and segmented using dilation for a morphological operation with morphological closing. Next, low pass Gaussian filter was applied to the image to smooth and reduce noises. It was found that our method improved the detection of microcalcifications and masses with high Peak Signal To Noise Ratio (PSNR).

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext Acoustic cardiac signals analysis: a Kalman filter-based approach

Salleh SH, Hussain HS, Swee TT, Ting CM, Noor AM, Pipatsart S, et al.

Int J Nanomedicine, 2012;7:2873-81.
PMID: 22745550 DOI: 10.2147/IJN.S32315

Auscultation of the heart is accompanied by both electrical activity and sound. Heart auscultation provides clues to diagnose many cardiac abnormalities. Unfortunately, detection of relevant symptoms and diagnosis based on heart sound through a stethoscope is difficult. The reason GPs find this difficult is that the heart sounds are of short duration and separated from one another by less than 30 ms. In addition, the cost of false positives constitutes wasted time and emotional anxiety for both patient and GP. Many heart diseases cause changes in heart sound, waveform, and additional murmurs before other signs and symptoms appear. Heart-sound auscultation is the primary test conducted by GPs. These sounds are generated primarily by turbulent flow of blood in the heart. Analysis of heart sounds requires a quiet environment with minimum ambient noise. In order to address such issues, the technique of denoising and estimating the biomedical heart signal is proposed in this investigation. Normally, the performance of the filter naturally depends on prior information related to the statistical properties of the signal and the background noise. This paper proposes Kalman filtering for denoising statistical heart sound. The cycles of heart sounds are certain to follow first-order Gauss-Markov process. These cycles are observed with additional noise for the given measurement. The model is formulated into state-space form to enable use of a Kalman filter to estimate the clean cycles of heart sounds. The estimates obtained by Kalman filtering are optimal in mean squared sense.

Matched MeSH terms: Signal-To-Noise Ratio
Fulltext Comparison of Low-Pass Filters for SPECT Imaging

Sayed IS, Ismail SS

Int J Biomed Imaging, 2020;2020:9239753.
PMID: 32308670 DOI: 10.1155/2020/9239753

In single photon emission computed tomography (SPECT) imaging, the choice of a suitable filter and its parameters for noise reduction purposes is a big challenge. Adverse effects on image quality arise if an improper filter is selected. Filtered back projection (FBP) is the most popular technique for image reconstruction in SPECT. With this technique, different types of reconstruction filters are used, such as the Butterworth and the Hamming. In this study, the effects on the quality of reconstructed images of the Butterworth filter were compared with the ones of the Hamming filter. A Philips ADAC forte gamma camera was used. A low-energy, high-resolution collimator was installed on the gamma camera. SPECT data were acquired by scanning a phantom with an insert composed of hot and cold regions. A Technetium-99m radioactive solution was homogenously mixed into the phantom. Furthermore, a symmetrical energy window (20%) centered at 140 keV was adjusted. Images were reconstructed by the FBP method. Various cutoff frequency values, namely, 0.35, 0.40, 0.45, and 0.50 cycles/cm, were selected for both filters, whereas for the Butterworth filter, the order was set at 7. Images of hot and cold regions were analyzed in terms of detectability, contrast, and signal-to-noise ratio (SNR). The findings of our study indicate that the Butterworth filter was able to expose more hot and cold regions in reconstructed images. In addition, higher contrast values were recorded, as compared to the Hamming filter. However, with the Butterworth filter, the decrease in SNR for both types of regions with the increase in cutoff frequency as compared to the Hamming filter was obtained. Overall, the Butterworth filter under investigation provided superior results than the Hamming filter. Effects of both filters on the quality of hot and cold region images varied with the change in cutoff frequency.

Matched MeSH terms: Signal-To-Noise Ratio

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