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Random multispace quantization as an analytic mechanism for BioHashing of biometric and random identity inputs

Teoh AB, Goh A, Ngo DC

IEEE Trans Pattern Anal Mach Intell, 2006 Dec;28(12):1892-901.
PMID: 17108365

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the Random Multispace Quantization (RMQ) of biometric and external random inputs.

Matched MeSH terms: Data Interpretation, Statistical
Thermal distribution analysis of three-dimensional tumor-embedded breast models with different breast density compositions

Wahab AA, Salim MI, Ahamat MA, Manaf NA, Yunus J, Lai KW

Med Biol Eng Comput, 2016 Sep;54(9):1363-73.
PMID: 26463520 DOI: 10.1007/s11517-015-1403-7

Breast cancer is the most common cancer among women globally, and the number of young women diagnosed with this disease is gradually increasing over the years. Mammography is the current gold-standard technique although it is known to be less sensitive in detecting tumors in woman with dense breast tissue. Detecting an early-stage tumor in young women is very crucial for better survival chance and treatment. The thermography technique has the capability to provide an additional functional information on physiological changes to mammography by describing thermal and vascular properties of the tissues. Studies on breast thermography have been carried out to improve the accuracy level of the thermography technique in various perspectives. However, the limitation of gathering women affected by cancer in different age groups had necessitated this comprehensive study which is aimed to investigate the effect of different density levels on the surface temperature distribution profile of the breast models. These models, namely extremely dense (ED), heterogeneously dense (HD), scattered fibroglandular (SF), and predominantly fatty (PF), with embedded tumors were developed using the finite element method. A conventional Pennes' bioheat model was used to perform the numerical simulation on different case studies, and the results obtained were then compared using a hypothesis statistical analysis method to the reference breast model developed previously. The results obtained show that ED, SF, and PF breast models had significant mean differences in surface temperature profile with a p value <0.025, while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model. The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique.

Matched MeSH terms: Data Interpretation, Statistical
Random walker with improved weighting function for interactive medical image segmentation

Yin LK, Rajeswari M

Biomed Mater Eng, 2014;24(6):3333-41.
PMID: 25227043 DOI: 10.3233/BME-141156

To segment an image using the random walks algorithm; users are often required to initialize the approximate locations of the objects and background in the image. Due to its segmenting model that is mainly reflected by the relationship among the neighborhood pixels and its boundary conditions, random walks algorithm has made itself sensitive to the inputs of the seeds. Instead of considering the relationship between the neighborhood pixels solely, an attempt has been made to modify the weighting function that accounts for the intensity changes between the neighborhood nodes. Local affiliation within the defined neighborhood region of the two nodes is taken into consideration by incorporating an extra penalty term into the weighting function. Besides that, to better segment images, particularly medical images with texture features, GLCM variance is incorporated into the weighting function through kernel density estimation (KDE). The probability density of each pixel belonging to the initialized seeds is estimated and integrated into the weighting function. To test the performance of the proposed weighting model, several medical images that mainly made up of 174-brain tumor images are experimented. These experiments establish that the proposed method produces better segmentation results than the original random walks.

Matched MeSH terms: Data Interpretation, Statistical*
Reproducibility of different laser Doppler fluximetry parameters of postocclusive reactive hyperemia in human forearm skin

Yvonne-Tee GB, Rasool AH, Halim AS, Rahman AR

J Pharmacol Toxicol Methods, 2005 Sep-Oct;52(2):286-92.
PMID: 16125628

Postocclusive reactive hyperemia in forearm skin is a commonly used model for studying microvascular reactivity function, particularly in the assessment of vascular effect of topically applied pharmacological substances. In this study, we investigated the reproducibility of several different laser-Doppler-derived parameters in the measurement of postocclusive reactive hyperemia at forearm skin in healthy subjects.

Matched MeSH terms: Data Interpretation, Statistical
Human evaluation in association to the mathematical analysis of arch forms: Two-dimensional study

Zabidin N, Mohamed AM, Zaharim A, Marizan Nor M, Rosli TI

Int Orthod, 2018 03;16(1):133-143.
PMID: 29478934 DOI: 10.1016/j.ortho.2018.01.009

OBJECTIVES: To evaluate the relationship between human evaluation of the dental-arch form, to complete a mathematical analysis via two different methods in quantifying the arch form, and to establish agreement with the fourth-order polynomial equation.
MATERIALS AND METHODS: This study included 64 sets of digitised maxilla and mandible dental casts obtained from a sample of dental arch with normal occlusion. For human evaluation, a convenient sample of orthodontic practitioners ranked the photo images of dental cast from the most tapered to the less tapered (square). In the mathematical analysis, dental arches were interpolated using the fourth-order polynomial equation with millimetric acetate paper and AutoCAD software. Finally, the relations between human evaluation and mathematical objective analyses were evaluated.
RESULTS: Human evaluations were found to be generally in agreement, but only at the extremes of tapered and square arch forms; this indicated general human error and observer bias. The two methods used to plot the arch form were comparable.
CONCLUSION: The use of fourth-order polynomial equation may be facilitative in obtaining a smooth curve, which can produce a template for individual arch that represents all potential tooth positions for the dental arch.

Matched MeSH terms: Data Interpretation, Statistical*
Testing the agreement of medical instruments: overestimation of bias in the Bland-Altman analysis

Zaki R, Bulgiba A, Ismail NA

Prev Med, 2013;57 Suppl:S80-2.
PMID: 23313586 DOI: 10.1016/j.ypmed.2013.01.003

The Bland-Altman method is the most popular method used to assess the agreement of medical instruments. The main concern about this method is the presence of proportional bias. The slope of the regression line fitted to the Bland-Altman plot should be tested to exclude proportional bias. The aim of this study was to determine whether the overestimation of bias in the Bland-Altman analysis is still present even when the proportional bias has been excluded.

Matched MeSH terms: Data Interpretation, Statistical