M. Anburajan

Thermal image analysis and segmentation of hand in evaluation of rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic inflammatory disease that affects and destroys the joints of fingers, wrist and feet. Although different imaging modalities like x-ray, magnetic resonance imaging and ultrasound are available for diagnosing the RA. Thermal imaging is considered as a novel imaging technique for diagnosing the RA. Thermal imaging technique is based on infrared Thermograms depicting the temperature variations in abnormal region of interest. The objectives of this study was i) to evaluate the rheumatoid arthritis based on heat distribution index and skin temperature measurements and to analyse the difference in skin temperature measurement in hand for RA patients and normal persons. ii) to automatically segment the abnormal regions of the hand especially for arthritis patients using fuzzy c means algorithm and Expectation Maximization (EM) algorithm. In this paper, thermal image analysis was done based on heat distribution index(HDI) and skin temperature measurement. The heat distribution value is obtained as 1.53±0.5 From the temperature analysis the results predicted was there is an increase in temperature of 0.96°c in hand region of RA patients compared to normal patients. The correlation between HDI and skin temperature measurement was statistically significant (r=0.63, p<;0.05). Fuzzy c-means algorithm has better results compared to EM Algorithm in evaluating the disease.

Changes of skin temperature of parts of the body and serum asymmetric dimethylarginine (ADMA) in type-2 diabetes mellitus Indian patients

In India, number of people with type 2 Diabetes Mellitus (DM) would be 87 million by the year 2030. DM disturbs autonomic regulation of skin micro-circulation, and causes decrease in resting blood flows through the skin. The skin blood flow has a major effect on its temperature. The aim of the study was to evaluate changes of skin temperature of all parts of the body and serum asymmetric dimethylarginine, ADMA (μmol/L) in type-2 DM Indian patients. Group-I: Normal (n=17; M/F: 10/15, mean±SD= 43.2±9.4 years); Group-II: Type-2 DM without cardiovascular (CV) complications (n=15; M/F: 10/7, mean±SD= 46.3 ± 14.0 years); Thermograms of all parts of the body were acquired using a non-contact infrared (IR) thermography camera (ThermaCAM T400, FLIR Systems, Sweden). Blood parameters and thyroid hormone were measured biochemically. Indian diabetic risk score (IDRS) was calculated for each subject. In type-2 DM patients without CV group (n=15), there was a statistically significant (p=0.01) negative correlations between HbA<inf>1c</inf> and skin temperature of eye and nose (r= −0.57 and r= −0.55 respectively). ADMA was correlated significantly (p=0.01) with HbA<inf>1c</inf> (r=0.65) and estimated average glucose, eAG (r=0.63). In normal subjects, mean minimum and maximum values of skin temperatures were observed at posterior side of sole (26.89°C) and ear (36.85°C) respectively. In type-2 DM without CV, mean values of skin temperature in different parts of the body from head to toe were lesser than those values in control group; but this decreases were statistically significant in nose (32.66 Vs 33.99°C, p=0.024) as well as in tibia (32.78 Vs 33.13°C, p=0.036) regions.

Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis

The aim of the study was (1) to perform an automated segmentation of hot spot regions of the hand from thermograph using the k-means algorithm and (2) to test the potential of features extracted from the hand thermograph and its measured skin temperature indices in the evaluation of rheumatoid arthritis. Thermal image analysis based on skin temperature measurement, heat distribution index and thermographic index was analyzed in rheumatoid arthritis patients and controls. The k-means algorithm was used for image segmentation, and features were extracted from the segmented output image using the gray-level co-occurrence matrix method. In metacarpo-phalangeal, proximal inter-phalangeal and distal inter-phalangeal regions, the calculated percentage difference in the mean values of skin temperatures was found to be higher in rheumatoid arthritis patients (5.3%, 4.9% and 4.8% in MCP3, PIP3 and DIP3 joints, respectively) as compared to the normal group. k-Means algorithm applied in the thermal imaging provided better segmentation results in evaluating the disease. In the total population studied, the measured mean average skin temperature of the MCP3 joint was highly correlated with most of the extracted features of the hand. In the total population studied, the statistical feature extracted parameters correlated significantly with skin surface temperature measurements and measured temperature indices. Hence, the developed computer-aided diagnostic tool using MATLAB could be used as a reliable method in diagnosing and analyzing the arthritis in hand thermal images.

Evaluation of rheumatoid arthritis in small animal model using Thermal imaging

Rheumatoid arthritis (RA) is a chronic autoimmune disease which affects the hand joints, wrist, feet, knee, shoulders and other regions of the body. Animal model of RA is used to evaluate the inflammatory condition and for diagnosing the disease. Although various imaging modalities like x-rays, CT and MRI are available in evaluation and diagnosing the disease, those modalities are expensive and have radiation effects. Thermal imaging plays a vital role in evaluation and monitoring the inflammation in rheumatoid arthritis Thermal imaging is a non invasive method for detecting the pathogenesis of the disease compared to other diagnostic methods. The objectives of this study were: i) to measure the changes in skin temperature of induced RA limbs when comparing to normal; ii) to find out the efficacy of an automated segmentation of induced RA limbs using the following image processing techniques: a) Robert edge detector; b) Canny edge detector; and c) Adaptive thresholding technique. 0.2ml of mixture of freunds complete adjuvant (FCA) mixed with a phosphate buffer saline at 1∶1 was injected at right side both hind- and fore- limbs of the Wistar rats (n=10), whereas normal saline was injected at left side both hind- and fore- limbs of the rats. After having induced RA in the limbs of the rats (after 30 days), thermal image of each rat was obtained under standard conditions. The measured mean temperature (°C) was found to be higher by 1.8% and 2.0% respectively at RA hind and fore limbs respectively than in the corresponding normal limbs. Adaptive thresholding method was found to be the best of method studied in segmenting the induced RA limb of the rat.

Estimation of blood glucose by non-invasive infrared thermography for diagnosis of type 2 diabetes: an alternative for blood sample extraction.

The present study aims to estimate and validate the glycated haemoglobin (HbA(1c)) using non-contact infrared thermography. The diagnostic threshold was set as (HbA(1c)≥48 mmol/mol). The optimal regression model [r=0.643, p=0.000] was achieved from the significant variables correlating with the HbA(1c) and the validation was performed against the bio-chemical assay to indicate the sensitivity, specificity, positive predictive value, negative predictive value and with an accuracy of [90%, 55%, 65%, 85% and 72%] respectively. The non-invasive core body temperature measurement at the inner canthi of eye [r=-0.462, p<0.01] indicated negative correlation with HbA(1c), that signifies the early metabolic changes. In type 2 diabetes, the core body temperature decreases with a decrease in the body metabolism. Thereby, a truly non-invasive infrared thermography could be used for obtaining the accurate HbA(1c) with no blood sample extraction; further, it could be used as the preferred diagnostic tool for type 2 diabetes.

Evaluation of Mammary Cancer in 7,12-Dimethylbenz(a)anthracene-Induced Wister Rats by Asymmetrical Temperature Distribution Analysis Using Thermography: A Comparison with Serum CEA Levels and Histopathology

Animal surface temperature profile captured using infrared camera is helpful for the assessment of physiological responses associated with the regulation of body temperature. Diagnosing breast cancer in early stage itself has a greater effect on the prognosis. In this work, asymmetrical temperature distribution analysis of chemical carcinogen 7,12-dimethyl benz(a)anthracene-induced in the lower right flank region of Wistar rats (n = 6) was carried out to test the potential of thermography in diagnosing mammary cancer and tumor growth over a period of nine weeks in comparison with histopathology results as standard. Temperature difference between the tumor induced lower right and left side of flank region was significant (with P value <0.001), whereas in the abdomen and shoulder there was no significant difference in temperature between right and left sides. Percentage of asymmetrical temperature difference in the tumor induced lower flank region was 0.5 to 2%, whereas in the other regions it was <0.5%. Green pixel distribution in RGB color histogram was asymmetrical in the tumor induced lower flank region. Temperature reduction was observed in the tumor induced region after the seventh day of carcinogen induction. Asymmetrical thermogram analysis is the best method of diagnosing mammary cancer and for studying tumor development.

Computer-based measurements of joint space analysis at metacarpal morphometry in hand radiograph for evaluation of rheumatoid arthritis

The aim and objectives are as follows: (i) to perform an automated segmentation of the hand from radiographs using a dual tree complex wavelet‐based watershed algorithm; ii) to compare the measured statistical features of the joint space of the hand using gray level co‐occurrence matrix (GLCM) method with standard diagnostic parameters of rheumatoid arthritis (RA).

Anthropometry: A new approach to identify communal body fat status in an urban south Indian population

BACKGROUND AND OBJECTIVES Deep penetration of obesity into geographical and ethnic communities based on profession is being highly evidenced by researchers. Impact of this penetration in the Indian urban population is addressed by the accepted factors of professional and cultural changes. High risk of Atherosclerosis, hyperinsulinaemia, impaired glucose tolerance; prothrombotic is not addressed by the relationship between BMI vs. body fat, while body fat plays major role in all risks. The present study attempts to prove an anthropometrical empirical formula which can be an indicator of body fat in a group, based on profession or life style. METHODS A total number of 159 (77 males of age 36.95 ± 14.795, 82 females of age 38.07 ± 13.16) subjects participated in the study. Body composition analysis and anthropometric measurements were performed after conducting careful clinical examination. Body fat was measured using body composition analyzer and used as a reference to justify indication of anthropometrical empirical indicator (AEI). Indicative accuracy of AEI was cross verified by male and female analysis individually. RESULTS Community specific mean body fat 23.15 ± 8.47 (kg) for the mean weight of 66.05 ± 13.46 (kg) indicated prevalence of excess 35% body fat. This much of body fat has not been addressed by mean BMI 25.56 ± 4.66 (kg/m(2)). CONCLUSIONS AND INTERPRETATION Statistical relation between AEI and body fat reflects original information of risk (where as BMI does not) in the selected community. AEI outperforms the identification of obesity affected profession or life style based communities over BMI analysis.

Multiparametric body composition analysis and anthropometric empirical indicator: Obesity based south Indian perspective

Background: Obesity has emerged to be a global threat to mankind. Many abnormalities such as cardiovascular diseases and diabetes emerge as outcomes of obesity. Objectives: The present study aimed at bringing out a technique which considers the combinational measurement of all essential anthropometric circumferences and body mass index (BMI), so that the accurate assessment of obesity can be made. To date, BMI has been considered to be the main adiposity index, but the distribution of body fat was not taken into account by BMI. The contradictory outcomes by BMI pertaining to risk factor detection in various ethnicities and populations were witnessed. Also, BMI failed to gauge obesity in muscular body builders who possess small waists and large torsos. Materials and methods: The study adopted a cross-sectional design and 107 subjects from urban south India participated. The measurements of body composition and anthropometry were shown. Results: The higher significant difference of ≤0.001 was observed in male and female studied population, when AEI (BIA1), AEI (EXTERNAL) and BMI were compared against BFM (measured by both the devices BIA1 and BIA2). Conclusions: The results exhibited the prominence of AEI (Anthropometric Empirical Indicator, which is the combinational measurement of all essential anthropometric circumferences and BMI) over BMI. Also, the validity of the effective functioning of low-cost, portable, simple protocol based body composition analyzer on par with the higher-cost, standard body composition analyzer was demonstrated by the present study.

Community-Specific BMI Cutoff Points for South Indian Females

Objective. To analyze multiparameters related to total body composition, with specific emphasis on obesity in South Indian females, in order to derive community-specific BMI cutoff points. Patients and Methods. A total number of 87 females (of age 37.33 ± 13.12 years) from South Indian Chennai urban population participated in this clinical study. Body composition analysis and anthropometric measurements were acquired after conducting careful clinical examination. Results. BMI demonstrated high significance when normal group (21.02 ± 1.47 kg/m2) was compared with obese group (29.31 ± 3.95 kg/m2), P < 0.0001. BFM displayed high significance when normal group (14.92 ± 4.28 kg) was compared with obese group (29.94 ± 8.1 kg), P < 0.0001. Conclusion. Community-specific BMI cutoffs are necessary to assess obesity in different ethnic groups, and relying on WHO-based universal BMI cutoff points would be a wrong strategy.