Extraction of medically interpretable features for classification of malignancy in breast thermography.

Thermography, with high-resolution cameras, is being re-investigated as a possible breast cancer screening imaging modality, as it does not have the harmful radiation effects of mammography. This paper focuses on automatic extraction of medically interpretable non-vascular thermal features. We design these features to differentiate malignancy from different non-malignancy conditions, including hormone sensitive tissues and certain benign conditions, which have an increased thermal response. These features increase the specificity for breast cancer screening, which had been a long known problem in thermographic screening, while retaining high sensitivity. These features are also agnostic to different cameras and resolutions (up to an extent). On a dataset of around 78 subjects with cancer and 187 subjects without cancer, that have some benign diseases and conditions with thermal responses, we are able to get around 99% specificity while having 100% sensitivity. This indicates a potential break-through in thermographic screening for breast cancer. This shows promise for undertaking a comparison to mammography with larger numbers of subjects with more data variations.

Semi-automated breast cancer tumor detection with thermographic video imaging.

Screening for breast cancer enables early detection by which curative treatment can be possible. While mammography is the current gold standard for screening, it has low sensitivity in younger women and its harmful X-rays in frequent screening can increase the risk of cancer. Incidence rates are rising in younger women, causing a relook at thermography for low cost and non-harmful screening. In this paper, thermography is compared to mammography correlated with sono-mammography in 65 FNAC/biopsy proven cancer subjects in India. Thermography is comparable to mammography correlated with sono-mammography, having 94% and 95% sensitivity, respectively. A novel semi-automated thermographic tumor detection and location algorithm used in this paper also provides 97% sensitivity. This shows the promise of automated thermographic screening for reaching large populations in a cost effective manner in low resource settings in countries like India. Further studies in a large scale need to be done to evaluate the specificity to enable such solutions.

Al2O3(1120) surface as a template for the ordered growth of Ni and Co nanoclusters.

The morphology and thermal stability of Ni and Co nanoclusters grown by physical vapour deposition on a reconstructed (1120) surface of α-Al(2)O(3) is investigated using non-contact atomic force microscopy (NC-AFM). NC-AFM images reveal that the clean α-Al(2)O(3)(1120) substrate adopts a characteristic (12 × 4) reconstruction when prepared in vacuum at high temperature. Subsequent deposition of Ni and Co onto this substrate at room temperature facilitates the growth of well-ordered metal nanocluster arrays with a preferred inter-cluster distance determined by the (12 × 4) periodicity of the substrate surface. The order in the cluster arrangement remains intact even upon annealing the system to temperatures up to 500 °C indicating a high resistance against sintering. The reconstructed α-Al(2)O(3)(1120) surface can, therefore, serve as an appropriate insulating template for studies of size-dependent magnetic or catalytic effects in a well-defined ensemble of metallic nanoclusters.

A high frequency sensor for optical beam deflection atomic force microscopy.

We demonstrate a novel electronic readout for quadrant photodiode based optical beam deflection setups. In our readout, the signals used to calculate the deflections remain as currents, instead of undergoing an immediate conversion to voltages. Bipolar current mirrors are used to perform all mathematical operations at the transistor level, including the signal normalizing division. This method has numerous advantages, leading to significantly simpler designs that avoid large voltage swings and parasitic capacitances. The bandwidth of our readout is solely limited by the capacitance of the quadrant photodiode junctions, making the effective bandwidth a function of the intensity of photocurrents and thus the applied power of the beam deflection laser. Using commercially available components and laser intensities of 1-4 mW we achieved a 3 dB bandwidth of 20 MHz with deflection sensitivities of up to 0.5-1 V/nm and deflection noise levels below 4.5 fm/Hz. Atomic resolution imaging of muscovite mica using FM-AFM in water demonstrates the sensitivity of this novel readout.

Unravelling the atomic structure of cross-linked (1 × 2) TiO2(110).

The cross-linked (1 × 2) reconstruction of TiO(2)(110) is a frequently observed phase reflecting the surface structure of titania in a significantly reduced state. Here we resolve the atomic scale structure of the cross-linked (1 × 2) phase with dynamic scanning force microscopy operated in the non-contact mode (NC-AFM). From an analysis of the atomic-scale contrast patterns of the titanium and oxygen sub-structures obtained by imaging the surface with AFM tips having different tip apex termination, we infer the hitherto most accurate model of the atomic structure of the cross-linked (1 × 2) phase. Our findings suggest that the reconstruction is based on added rows in [001] direction built up of Ti(3)O(6) units with an uninterrupted central string of oxygen atoms accompanied by a regular sequence of cross-links consisting of linear triples of additional oxygen atoms in between the rows. The new insight obtained from NC-AFM solves previous controversy about the cross-linked TiO(2)(110) surface structure, since previously proposed models based on cross-links with a lower O content do not appear to be consistent with the atom-resolved data presented here. Instead, our measurements strongly support the Ti(3)O(6) motif to be the structural base of the cross-linked (1 × 2) reconstruction of TiO(2)(110).

Biometric verification with correlation filters.

Using biometrics for subject verification can significantly improve security over that of approaches based on passwords and personal identification numbers, both of which people tend to lose or forget. In biometric verification the system tries to match an input biometric (such as a fingerprint, face image, or iris image) to a stored biometric template. Thus correlation filter techniques are attractive candidates for the matching precision needed in biometric verification. In particular, advanced correlation filters, such as synthetic discriminant function filters, can offer very good matching performance in the presence of variability in these biometric images (e.g., facial expressions, illumination changes, etc.). We investigate the performance of advanced correlation filters for face, fingerprint, and iris biometric verification.