This manuscript presents an analytical treatment on the feasibility of multi-scale Gabor filter bank response for non-invasive oral cancer pre-screening and detection in the long infrared spectrum. Incapability of present healthcare technology to detect oral cancer in budding stage manifests in high mortality rate. The paper contributes a step towards automation in non-invasive computer-aided oral cancer detection using an amalgamation of image processing and machine intelligence paradigms. Previous works have shown the discriminative difference of facial temperature distribution between a normal subject and a patient. The proposed work, for the first time, exploits this difference further by representing the facial Region of Interest(ROI) using multiscale rotation invariant Gabor filter bank responses followed by classification using Radial Basis Function(RBF) kernelized Support Vector Machine(SVM). The proposed study reveals an initial increase in classification accuracy with incrementing image scales followed by degradation of performance; an indication that addition of more and more finer scales tend to embed noisy information instead of discriminative texture patterns. Moreover, the performance is consistently better for filter responses from profile faces compared to frontal faces.This is primarily attributed to the ineptness of Gabor kernels to analyze low spatial frequency components over a small facial surface area. On our dataset comprising of 81 malignant, 59 pre-cancerous, and 63 normal subjects, we achieve state-of-the-art accuracy of 85.16% for normal v/s precancerous and 84.72% for normal v/s malignant classification. This sets a benchmark for further investigation of multiscale feature extraction paradigms in IR spectrum for oral cancer detection.
Automated face detection is the pivotal step in computer vision aided facial medical diagnosis and biometrics.
This paper presents an automatic, subject adaptive framework for accurate face detection in the long infrared
spectrum on our database for oral cancer detection consisting of malignant, precancerous and normal subjects
of varied age group. Previous works on oral cancer detection using Digital Infrared Thermal Imaging(DITI)
reveals that patients and normal subjects differ significantly in their facial thermal distribution. Therefore, it is
a challenging task to formulate a completely adaptive framework to veraciously localize face from such a subject
specific modality. Our model consists of first extracting the most probable facial regions by minimum error
thresholding followed by ingenious adaptive methods to leverage the horizontal and vertical projections of the
segmented thermal image. Additionally, the model incorporates our domain knowledge of exploiting temperature
difference between strategic locations of the face. To our best knowledge, this is the pioneering work on detecting
faces in thermal facial images comprising both patients and normal subjects. Previous works on face detection
have not specifically targeted automated medical diagnosis; face bounding box returned by those algorithms are
thus loose and not apt for further medical automation. Our algorithm significantly outperforms contemporary
face detection algorithms in terms of commonly used metrics for evaluating face detection accuracy. Since our
method has been tested on challenging dataset consisting of both patients and normal subjects of diverse age
groups, it can be seamlessly adapted in any DITI guided facial healthcare or biometric applications.
Histopathology is considered the gold standard for oral cancer detection. But a major fraction of patient pop- ulation is incapable of accessing such healthcare facilities due to poverty. Moreover, such analysis may report false negatives when test tissue is not collected from exact cancerous location. The proposed work introduces a pioneering computer aided paradigm of fast, non-invasive and non-ionizing modality for oral cancer detection us- ing Digital Infrared Thermal Imaging (DITI). Due to aberrant metabolic activities in carcinogenic facial regions, heat signatures of patients are different from that of normal subjects. The proposed work utilizes asymmetry of temperature distribution of facial regions as principle cue for cancer detection. Three views of a subject, viz. front, left and right are acquired using long infrared (7:5 - 13μm) camera for analysing distribution of temperature. We study asymmetry of facial temperature distribution between: a) left and right profile faces and b) left and right half of frontal face. Comparison of temperature distribution suggests that patients manifest greater asymmetry compared to normal subjects. For classification, we initially use k-means and fuzzy k-means for unsupervised clustering followed by cluster class prototype assignment based on majority voting. Average classification accuracy of 91:5% and 92:8% are achieved by k-mean and fuzzy k-mean framework for frontal face. The corresponding metrics for profile face are 93:4% and 95%. Combining features of frontal and profile faces, average accuracies are increased to 96:2% and 97:6% respectively for k-means and fuzzy k-means framework.
In recent years photonic crystals have become a favored area of research due to their diversified applications. In this
paper we propose a mathematical model for analyzing the photonic band gap of a 1D binary photonic crystal (GaAs and
air) which allows us to use it effectively as a photonic tuner which is an integral part of any optical amplifier. As optical
parameters like reflection and refraction follows similar pattern from each plane within a photonic crystal, we can take
help of characteristic matrix for a single plane and multiply (m) times where the crystal consists of (m) periods. Using
the fact that the characteristic matrix comes out to be unimodular and taking help of Cayley-Hamilton theorem and
Chebyshev polynomials, we expand the matrix of the entire system to derive the location and width of photonic band
gaps. Higher stop bands occur at lower frequency of incoming radiation and central bandgap wavelength decreases with
increasing angle of incidence. The power transmitted by the tuning crystal decreases for radiations away from normal.
Using a polarizer model, the attenuation is computed to be proportional to log|Cos2θ|, where θ is the angle of incidence.
The mathematical modeling developed can also be extended for realization of n-array photonic crystal. We have also
considered the refractive index modulation with respect to temperature for using it as a temperature sensor.