Dynamic infrared imaging for the detection of malignancy

Button, Terry M.; Li, Haifang; Fisher, Paul R.; Rosenblatt, Ruth; Dulaimy, Khaldoon Al; Li, Song; O’Hea, Brian; Salvitti, Mathew; Geronimo, Veronica; Geronimo, Christine; Jambawalikar, Sachin; Carvelli, Paola; Weiss, Richard C.

doi:10.1088/0031-9155/49/14/005

Cited by 40 publications

(39 citation statements)

References 16 publications

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“…9. The range of the analyzed frequency is between 0.1 Hz (vasomotor frequency) and 1 Hz (cardiogenic frequency) [16]. High frequency components of the image, where the power of the time−temperature signal is higher due to larger temper− ature fluctuation, is highlighted by dark red [ Fig.…”

Section: Spectral Analysis Of Temperature Modulationmentioning

confidence: 99%

Comparison of time-series registration methods in breast dynamic infrared imaging

Riyahi-Alam

Agostini

Molinari

et al. 2015

Opto-Electronics Review

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Automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time−temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. The aim of this work is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user−independent. We implemented and evaluated 3 different 3D time−series registration methods: 1. Linear affine, 2. Non−linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70 ±0.03, 0.74 ±0.03 and 0.81 ±0.09 (out of 1) for Affine, Bspline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons’ registration method outperforms also with the best breast alignment and non−negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons’ registration as an effective technique for time−series dynamic infrared registration, to stabilize the local temperature oscillation.

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Section: Spectral Analysis Of Temperature Modulationmentioning

confidence: 99%

Comparison of time-series registration methods in breast dynamic infrared imaging

Riyahi-Alam

Agostini

Molinari

et al. 2015

Opto-Electronics Review

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show abstract

“…To quantify changes in the outline (edges or contours) of image regions, the FS descriptor is used, because these descriptors are not invariant to scaling and translation [59]. For any edge, within an image, having coordinates (X i , Y i ), where i = 1, 2, ..., K represent the edge points of an object, the FS descriptors are denoted by the following expression:…”

Section: Fourier Spectrum (Fs) Descriptorsmentioning

confidence: 99%

“…Table 3 details the work published on CAD systems for breast cancer detection. [161] 2007 2a 1 ---Jakubowska et al [162] 2003 4b 1 ---Ng et al [163] 2002 3b 3 61.54 68.97 40 Frize et al [164] 2002 2b 1 ---Kuruganti and Qi [165] 2002 3b 1 ---Ng et al [166] 2001 3b 2 59 54 67 Ng et al [167] 2001 3b ----Keyserlingk et al [168] 1998 2a 1 ---Thompson et al [169] 1978 2b 1 ---Folberth and Heim [170] 1984 2a [172] 2013 2a 1 ---Garcia-Romero et al [174] 2013 2a 1 ---Shada et al [173] 2013 2a 2 -95 100 Cholewka et al [175] 2012 2a 1 ---Flores-Sahagun et al [171] 2011 1a 1 ---Aweda et al [176] 2010 2a 1 ---Buzug et al [177] 2006 3a 1 ---Button et al [59] 2004 2a 1 ---…”

Section: Breast Cancermentioning

confidence: 99%

Application of infrared thermography in computer aided diagnosis

Faust

Acharya

et al. 2014

Infrared Physics & Technology

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The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even nonlinear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based ComputerAided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care.

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“…The multiplied rhythms are reflected between frequency levels 2.4Á2.8 and 3.5Á4. 5 Hz, and come out at about 2Á3-fold greater frequencies than cardiogenic rhythm (2).…”

mentioning

confidence: 92%

“…The average emissivity of the human body is 0.98. The skin is therefore an almost perfect radiator, emitting only the thermal radiation transmitted by itself, which offers the potential for medical application of IR imaging (2,3). Skin temperature modulation is based mainly on autonomous nervous control of cutaneous and subcutaneous perfusion, which is affected by local and systemic pathological conditions.…”

mentioning

confidence: 99%