International Journal of Pure and Applied Mathematics Volume 120 No. 6 2018, 15-26 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/
DETECTION OF ANEMIA DISEASE USING PSO ALGORITHM AND LBP TEXTURE ANALYSIS
1S. Dhanasekaran M.E., 2Dr. N. R. Shanker Ph.D., 1Research Scholar, 2Professor/ Supervisor-Aalim Muhammed Salegh College of Engineering Department of Electronics and Communication Engineering PRIST University, Thanjavur, Tamilnadu
Abstract: Nowadays, patients with anemia disease oxygen from the lungs to different parts of the body and present in the world increased by around 60-70% also to carrying maximum carbon dioxide (CO2) from respectively. The digital image processing technique has different parts of the body to lungs. successfully characterised to introduce new methods for Functional near-infrared spectroscopy (fNIRS) is disease analysis has lead to reliable systems and more utilised to differentiatethe patient with schizophrenia, and accurate for anemia disease diagnosis. This paper gives the healthy persons are based on the support vector an algorithm for the automatic detection of anemia machine (SVM) and principal component analysis disease through palm image. For solving such issues, a (PCA). Firstly, PCA is utilized to select the features on PSO algorithm and LBP texture analysis are applied for oxygenated haemoglobin (oxy-Hb) signals from the classification of palm images. There are several features different channel fNIRS data. Secondly, aextraction is are consider based on statistical analysis, i.e. mean, based on SVM is planned to separate the schizophrenia variance and entropy have been extracted. The from a healthy people. Finally, the method gives an classification results demonstrate that these features accuracy of 93.33%, 84.62% for healthy people and highly import and can be utilised to identify normal and 100% for schizophrenia. AfNIRSmethod hasa potential abnormal patients 98% successfully. capacity and an effective aim biomarker for the analysis of schizophrenia [1]. Keyword: Digital image processing, Anemia disease Leukaemia patient’s presents with reduced diagnosis, PSO Algorithm and LBP Texture analysis. haemoglobin and the WBC count in about 60-70% of cases. Peripheral blood smear (PBS) method brings out 1. Introduction about 40-95% of blast cells in leukaemia patients. The digital image processing method has successfully lead to Anemia is a disease, due to the lack of the total amount developing new techniques for cell diagnosis has lead to of haemoglobin or red blood cells (RBCs) in the blood, more reliable and accurate systems for disease analysis. or a brought capacity of the blood down to carry However, high differences in cell size, edge, shape and oxygen.Red blood cells (are also called as erythrocytes) localisation make more complex the data extraction are the most basic type of blood cell, and the vertebrate process. The electromagnetism-like optimisation (EMO) organism’s principal entails of delivering oxygen (O2) to algorithm introduced to detect automatically white blood the body tissues via the blood flow by the circulatory cells embedded into intricate smear an image that takes system. They absorb oxygen in the gills or lungs and the total function as a circle detection problem. The EMO release it while forcing by the body’s capillaries. technique gives a result from blood cell images with a Haemoglobin (Hb) is a blood content containing protein changing range of complication are admitted to formalise and iron. The human beings become unhealthy while Hb the efficiency concerning detection, stability and ranges in their blood level are reduced to a certain fixed robustness[2]. limit as for females is 11 mg/dL and for males is 13 Blood cell categorisation is the beginning process for mg/dL.There are different stages of this health problem finding disease; the diseases can be contained if it is and are consequently called severe anemia, moderate detected at the starting stage. To solve problems, anemia and mild anemia. The decrease in Hb level in quantitative processing of digital images based on a fuzzy blood is because of the deficiency of folic acid, vitamin method is introduced for categorisation of red blood B12, or iron. Nowadays, anemia occurs due to the cells. There are different features consist of size, shape deficiency of iron is normally very common. So, a and colour based features, that based on statistical decrease in the iron level will outcome in decreased analysis (i.e. kurtosis, roundness, skewness, mean, oxygen carrying capacity of the blood, which can hurt the variance, standard deviation) have been extracted. The health of people. In blood, Hb is worthy of carrying categorisation results showed that features significance
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highly and can be utilised for categorisation of red cells uncertainties, and then we can extract the to the normal and up normal cells. The result presents a knowledgeabout the component which creates diabetes. 98% of red blood cells successfully[3]. From the analysis, BMI will be the biggest influencer Anemic status in children and women all over the about HbAlc changes. The first step for maintaining the world area important things for concern. In human blood, health is the daily monitoring. A MEMS-based small and haemoglobin is measured by a standard technique is adaptable monitoring devicehas been created by the cyanmethemoglobinand the world health organisation ERATO maenaka human-sensing fusion design. We (WHO) is also recommended as a well-recognised developed a condition estimation strategy utilising the method. While comparing to this method, there are monitoring device and FNN-based condition estimation. several methods available with an approximate result. The experiment results demonstrate that it is a promising One among the method is colour-measurement technique, strategy for health condition understanding. One of the and in low resource settings adaptation, the WHO important lifestyle diseases iscerebral vascular and is recommended this technique.Human interpretation errors caused by cerebral aneurysms. To detect the disease in are probably moved slowly in during the subjective the forecast, we should analyse aneurysms and cerebral process required with this colour-measurement technique, arteries utilising magnetic resonance angiography images in human blood Hb count is measured by an artificial [6]. neural network (ANN) method. The ANN utilised input A novel technique is utilised to extract haemoglobin as the Hb value and the colour-coded values of the and melanin centralisations of human skin, utilising samples, as received with the cyanmethemoglobin bilateral decomposition with the information of a technique, as trusted output. The results demonstrate a numerous layered skin model and absorbance attributes warm relation among the Hb level in the blood and the of major chromophores. The proposed method is colour of the blood sample [4]. different from the state-of-art method, is to address the Thalassemia is an inherent issue of haemoglobin feature and strong shadinggenerally in existing systems blend, which can lead to stroke in the brain and the skin colour images are caught under the uncontrolled thromboembolic case. In this work, utilising a functional conditions. The determined haemoglobin and melanin connectivity model to separate amongst diseased subject files, specifically identified with the pathological tissue and control. Our connectivity measure depends on conditions, tend to be less impacted by external imaging functional magnetic resonance imaging, and hence the factors and are efficient in distinguishing pigmentation regular changes of the blood oxygenation level in distributions. Experiments show the estimation of the spatially removed areas. Examining this connectivity proposed technique for computer-aided analyses of could feature unusual neuronal activation and give us a various skin diseases. The analyses exactness of descriptor (bio-marker) of the infection. To estimate the melanoma raises by 9-15% for conventional RGB lesion connectivity, developed a robust learning method in light images, equated to the techniques utilised early colour of the graphical lasso model, whose hyperparameter is descriptors. The of hyperpigmentation reveals acne and approved inside a cross-validation scheme. To examine inflammatory acne stage, which would be utilised for model fit, we exchange the mean property from the acne severity valuation[7]. control group to the thalassemic patient group. Null Basal cell carcinoma (BCC) is the mostbasic type of hypothesis model is learned from control subjects, and it skin cancer. Analysis of BCC is an essential factor in the is equally suitable ( in the likelihood sense) to explain to forecast of the disease.The vascular structures of the the patients. The permutation test results give that the few lesion are the important key for BCC analysis. Discovery patients with thalassemia do not have the same and realisation of cutaneous vasculature give basic data connectivity structure as the control[5]. on the determination of exactness and evaluation To predict and prevent the lifestyle disease utilising accuracy. An effective method is demonstrated to daily monitoring the health, periodical health checkup separate vascular data towards lesion analysis. Given a and detection through medical imaging. Here, three new dermoscopy image, initially vascular structures of the approaches have been introduced to the structural lesion segmented by decomposing the image utilising analyseof healthcheckups. The first method isthe fuzzy independent component analysis into haemoglobin and set, and it converts the overall properties of the health melanin components and then implementing shape filters checkup data into fuzzy degrees by characterising fuzzy at various scales. A vessel mask is produced as an output membership functions. The second method investigates of global thresholding. An arrangement of vascular relationships between qualities of particular health features are then separated from the last vessel image of examination data to adapt to the way of lifestyle diseases. the lesion and flowed into a random forest classifier. The It utilises self-organising maps and clears up the technique shows the execution of 90.3% regarding AUC relationship among glutamic-oxaloacetic transaminase, in distinguishing BCC from progressive lesions [8]. haemoglobin Alc (HbAlc), gamma- Wounds that do not succeed a certain track of glutamyltranspeptidase, glutamic-pyruvic transaminase, healing within a determined period form into ulcers and triglyceride. The third method predicts HbAlc making severe pain and uneasiness the patients. While vacillations utilising decision tree. If we can predict the wounds are healing, one of the most noticeable changes
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in the colour of the tissues. In a wound healing appraisal, manual system and hence calculate the hematocrit level reporting all the tissues based on the percentage of the more accuracy [11]. tissue colour is an approved clinical technique. The Pallor or paleness is a demonstration of blood loss or development of the red granulation tissue denotes the low haemoglobin absorption in the human blood that can starting stage of ulcer healing. Granulation tissue seems be originated by pathologies such as anemia. The red because of haemoglobin circumstance in the blood automated screening process is presented that use pallor vessels. An approach founded by using haemoglobin site images, segments, and removes colour and intensity- circumstance in chronic ulcers as an image marker to based features for multi-class categorisation of patients identify the development of granulation tissue. with high pallor because of anemia-like pathologies, Independent component analysis is utilised to remove patients with normalities, and patients with other grey-level haemoglobin images from RGB colour images abnormalities. This work examines the pallor locales of of chronic ulcers. Removed haemoglobin images conjunctiva and tongue for pallor screening purposes. demonstrate the region of haemoglobin distribution The conjunctiva and sclera regions are consequently reflecting observed regions of granulation tissue. Data sectioned for regions of interest because of to get the eye clustering methods are executed to sort and segment pallor site images. Also, the inner and outer tongue observed regions of granulation tissue from the removed locales are sectionedto get the tongue pallor site images. haemoglobin images. Results acquired demonstrate that Color-plane based feature extraction is executed the introduced algorithm performs genuinely well with an accompanied by machine learning algorithms for feature average affectability of 88.24% and specificity of 98.82% decreasing and image level categorisation of anemia. A when contrasted with the dermatologist evaluation. The suite of characterisation algorithms image-level orders main aim is to build up a non-invasive wound healing for normal (class 0), pallor (class 1) and different appraisal framework ability to appraise the healing variations from the norm (class 2). The proposed condition of chronic ulcers in a more exact and authentic techniques accomplish 86% exactness, 85% precision way [9]. and 67% review in eye pallor site images and 98.2% For ulcers patients, severe pain and discomfort are precision and accuracy with 100% review in tongue caused by the chronic wounds. Depicting the ulcer tissues pallor site images for the arrangement of images with in the condition of rates of each tissue colour is an pallor. The developed pallor screening method can be affirmed strategy for wounds healing evaluation. The further tuned to identify the seriousness of anemia-like development of the red granulation tissue is the primary pathologies utilising controlled set of local images that pointing of the ulcer healing from the base of ulcers. would then be able to utilise for future benchmarking Granulation tissue seems red because of the haemoglobin intend [12]. setting in the blood vessels. The main goal is to study the There are a few diseases, for example, anemia that is optical features of haemoglobin setting in ulcers as more feared, particularly in pregnant ladies and kids. conceivable image marker in distinguishing granulation Analysis of these diseases needs regular blood tests. The tissue from RGB colour image of chronic ulcers. conclusion requires refined equipment with a trained Independent component analysis is applied to remove specialist. In a created world this does not represent an grey-level haemoglobin images from RGB colour images issue, but rather in creating the world, this is an issue. In of chronic ulcers. Removed haemoglobin images reflect numerous rural settings, this installation is not accessible. the region of haemoglobin distribution mentioning to the The non-availability postures certain health risks. So distinguished region of granulation tissue. K-implies there is a requirement for a simpler and effective strategy clustering is enforced to characterise and segment to analysis these diseases. It is discovered that shade of distinguished regions of granulation tissue. Preliminary blood can establish a denotation to the seriousness of examination of the outputs demonstrates an overall these diseases. In our access, we recommend non- accuracy of 97.31% of the algorithm execution at the intrusive analysis if anemia [13]. point when contrasted with manual segmentation [10]. Erythrocytes are the fundamental segment of the Hematocrit level estimates the rates of red blood cells in blood and comprise haemoglobin. The fundamental the blood. The estimation of hematocrit level works an function of erythrocytes is to transport carbon-di-oxide essential diagnostic part in numerous diseases like an from tissues to lungs and oxygen from lungs to tissues. ulcer, colon cancer, bone marrow disorder, anemia, etc. At the point when the size of erythrocytes changes then The conventional process of estimating the hematocrit they frame poikilocyte cells. Iron deficiency anaemia is level in pathological labs is slow and inclined to manual identified by poikilocytes, for example, elliptocyte, errors. The automated system has been introduced for the schistocyte, dacrocyte, degmacyte. Iron is fundamental estimation of hematocrit level from the total red blood for the creation of haemoglobin, with its deficiency, cell count in blood. The relation based on similarities and erythrocytes size become smaller, alter their shape and differences between the hematocrit levels are found by turns paller in colour than normal. The normal the traditional method, and one of the methods was poikilocyte cells and RBC have been separated utilising introduced to estimate the appropriateness. The the artificial neural network, founded by the extracted introduced system could defeat the retreat existing in the features. At the moment, blood trouble is recognised by
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the visual audit of digital images by studying changes in the size, colour and statistical study of digital images. Pre-processing, morphological operations, segmentation, feature extraction and categorisation steps have been connected to the blood smear microscopic images to recognise the cells. A detachment of covered cells has been done efficiently, accurately and automatically [14]. Morality in autonomic control in sickle cell anemia (SCA) patients has been described by various analysts. However, their potential causal relationship with sickle cell danger situation stays unknown. We utilised hypoxia, a known trigger to sickle cell danger situation, to irritate the autonomic systems of the subjects. Cardiac, vascular autonomic control was noninvasively evaluated by tracking the adjustments in heart rate variability (HRV) that happen following a brief introduction to a hypoxic stimulus. Time-varying spectral study of HRV was connected to calculate the cardiac autonomic function to the transient scene of hypoxia. The results exhibit that Figure 1. Block diagram cardiovascular autonomic function to hypoxia is . considerably more delicate in SCA than in ordinary 3.1 Filter Image controls. We also introduced a model to adjust for the confounding effects of respiration on the HRV spectral Filter image is a pre-processing stage to remove noise ratio by utilising the relating respiration signal to adjust and to improve the features. We use an efficient Gaussian for the correlated respiratory portion of the HRV. This filter to decrease the effect of camera noise and dirt while method enhanced the determination with which the conserving the edge information needed for processing impact of hypoxia on changes in HRV could be estimated palm images. By utilising Fourier transform, an image is [15]. decomposed into sine and cosine factor. The two- dimensional discrete Fouriertransform is performed at the size of t he N × N image and as given as N−1 N−1 s t 2. Inferences from the Literature Survey −i2 π l + m ( N N ) F ( s ,t ) =∑ ∑ f ( l ,m ) e Previously, a fuzzy logic algorithm is utilised to analyse l=0 m=0 the elements of human blood and the image treatment, (1) then the features of the samples are classified and However, the intensity value of a Fourier transform will statically analysed.However, it has less efficiency in the be too large, and it is difficult to display on the screen, detection of anemia disease. So, the proposed system the other value appears as black in the figure. presents the results from the LBP texture analysis and PSO algorithm to enhance the anemia disease in patients 3.2 Local Binary Patterns (LBP) with various ages. To detect the disease palm image has been utilised in this process. Local binary patterns (LBP) are proposed for texture description. The operator was effectively connected to 3. Methodology certain problems, for example, people and face detection. A fundamental LBP feature is held from a 3x3 Digital microscope with the high-resolution image sensor neighbourhood of each pixel, and a threshold value is is used to capture the palm image of a patient. It is easy utilised, contrasting the central pixel and each neighbour to zoom, magnification and display minimus details of and the output is encoded as a binary label. Normally the the image. To estimate the anemia disease from the histogram of the outcoming labels is then utilised as an patient, we developed four stages: i) filter image, ii) image descriptor. The operator is expanded to utilise LBP-texture analysis iii) PSO algorithm iv) Histogram neighbourhoods of dissimilar sizes by applying a circular analysis. As our four stages integrate multiple approaches neighbourhood across the centre pixel and bilinearly in palm images to combine and identify the disease altering the pixel values. The central pixel value in the through statistical analysis image, a pattern number is calculated by matching its value with their neighbourhoods. A−1 A LBP A, B=∑ s ( gu−gv ) 2 (2) A=0 s ( x ) = 1, x≥ 0 {0, x<0 (3)
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Where gv is the grey value of the central pixel, gu is the value of its neighbours; A is the number of PSO is one of the best solution methods which is widely neighbours and B is the radius of the neighbourhood. utilised in optimisation problems. PSO is notable for its Imagine the coordinates of gv are (0, 0), then the fast exploitation/exploration in the research space instead coordinates of gurepresented by of greedy research techniques. So, between various (−Bsin ( 2πA / A ) ,Bcos ( 2πA / A ) ). The grey values of evolutionary algorithms, PSO is selected here, because of neighbours, which are not present at the centre of grids, its fast5 convergence also regarding both global and local can be determined by altering. fitness of each particle. Imagine the texture image is N×M. After describing This algorithm at the starting stage generates a the LBP pattern of each pixel (i, j), the entire texture population of examinee solution, are called as particles. image is staged by developing a histogram: They are arbitrarily created, and all particle of this N M population has possible proper solutions, attains a striking score in the fitness standard, after many epochs. H ( k )=∑∑ f ( LBP A ,B ( i, j ) ,k ) ,k∈ [ 0,k ] (4) i=1 j=1 During iteration, particles are optimised below two f ( x , y ) = 1,x= y different standards: gbest and pbest which, severally, {0,ot hrwise (5) evaluate the global and local fitness of a particle. The ith Where the maximum LBP pattern value is K. If the particle refers to Xi =( xi 1 ,xi 2 ,…………,❑Xis ) where S rotation is fixed to integer multiples of the angle among is the dimension. The position and velocity of an two sampling points, i,e. individual particle are modified to the following relation: 360. v =w∗v +c∗rand ( . )∗ p −x +c ∗Rand ( . )∗ p −x ∝=a , a=0,1,……, A−1,this rotates the sampling id id ( id id ) 2 ( gd id ) A (10) neighbourhood by precisely a discrete steps. So the Where d=1,2,...... , S, w is the inertia weight, c1 and c2 uniform pattern U A ( n ,r )at point (x, y) is interchanged by are the acceleration constitute the weighting of the ¿ ¿ uniform pattern U A ( n ,r+amod A ) at point ( x , y ) of stochastic acceleration condition that attracts individual the rotated image. particles for pbest ( pid) and gbest ( pgd ¿ positions. As noticed in the preceding word, rotations of a Rand ( . ) and rand ( . ) are two random functions in the texture input induce the LBP patterns to render into range of [0,1]. The velocity of all individual particle (v) various locations and to rotate about their origin. is reduced among vmin to ❑Vmax which delimited by the Calculating the histogram of LBP codes become normal user as input parameters that calculate the step size of all for translation, and rotation invariant mapping attains individual particle through the result space. The PSO normalisation for rotation. During mapping, all LBP algorithm procedure is given below as: binary code is rotated into its minimum value. Initialize swarms, generate m particles bi min Pbest (i)=0; i=1,.....,m.d=1,.....,S LBP A, B= ROR ( LBP A, B, i ) (6) i Gbest=0; Iter=0; Where ROR(x,i) refers to the circular bitwise right of While (Iter
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t j ip u = i (12) j ∑ w i=t j−1+1 j 2 To calculate the within-class (σ w ) and among-class 2 (σ B ) variance as n+1 n+1 2 2 2 2 σ w=∑ w j σ j , σ B=∑ W j ( uj −ut ) (13) j=1 j=1 2 Where class variance σ jis calculated as t i 2 n+1 2 (i−uj ) pi 2(b) σ j = ∑ , and ut =∑ w j u j gives the total w j i=t j−1+1 j=1 mean of the grey scale levels. The entropy of the class is determined as t j h j=− ∑ pi .ln pi (14) i=t j−1+1 Where pi represents the probability of grey scale level N hi i. pi= ,∑ pi=1and i stand for a specific intensity N i=1 level, i.e., 0≤ i≤ L−1, N stands for the total number 2(c) pixels in the palm image and h j stands for the number of pixels for the representing intensity level i in the grey image.
4. Results and discussion
The experimental tests have been introduced in order to calculate the performance of the anemia disease patient. It was tested over digital microscopic images from the palm region of anemia disease patients with high pixel resolution. The palm images demonstrate several complex conditions of the patients and have been 2(d) examined by the LBP texture analysis and PSO algorithm. Here, the two different patient images have demonstrated for a sample, but more than 50 number of patients detail has collected and mentioned the value in the table, concerning the algorithm utilised. The outcomes demonstrate that the proposed algorithm can detect the anemia disease efficiently. The parameters calculated through the algorithm are mean, variance and entropy value of several images.
2(e)
2(a)
2(f)
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Figure 2. the resulting images of the first stage after patient, (d) LBP texture analysis image for the abnormal applying the filter. (a) the original image for normal patient. patient, (b) grey scale image for normal patient, (c) filter image for normal patient, (d) original image for abnormal patient, (e) grey scale image for abnormal patient, (f) filter image for the abnormal patient.
4(a)
3(a)
4(b)
Figure 4. The palm image analysed using PSO algorithm 3(b) (a) normal (b) abnormal
3(c) 5(a)
3(d)
Figure 3. (a) LBP histogram analysis for a normal patient, (b) LBP texture analysis image for normal patient, (c) LBP histogram analysis for the abnormal
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The table 1 and 2 demonstrate the results of the statistical analysis in equations (1 to 14) on 50 images presenting normal and abnormal anemia patients palm image samples. In table 1, LBP texture analysis the input image and gives mean, variance and entropy value. The entropy value, which is greater than 3.4 above, will be abnormal and the patient has anemia disease. In table 2, PSO algorithm analysis the input image and gives mean, variance and entropy value. The entropy value between 1.1-1.2 will be abnormal, and the patient has anemia disease.
5. Conclusion
We developed a system for identification of anemia 5 (b) disease based on the digital image processing technique as LBP texture analysis and PSO algorithm using Figure 5. Histogram analysis (a) normal patient (b) MATLAB program; the output gives more accuracy of abnormal patient 98% for the anemia disease identification between patients as normal and abnormal. In this study, the Table 1. LBP texture analysis value for detecting anemia statistical analysis plays an important factor to identify disease the infected person efficiently.
Patient Mean Standard Entropy References number value deviation value value [1] Z. M. Abood, “Classification of Red Blood Cells P1 41.5211 63.0704 3.4116 Disease Using Fuzzy Logic Theory,” Int. Conf. Curr. P2 40.7228 61.3183 3.3965 Res. Comput. Sci. Inf. Technol., pp. 31–36, 2017. P3 44`4805 66.9406 3.5290 [2] G. Biji and S. Hariharan, “An efficient peripheral P4 40.5985 61.9831 3.3908 blood smear image analysis technique for Leukemia P5 53.1203 73.7448 3.6484 detection,” Proc. Int. Conf. IoT Soc. Mobile, Anal. P6 43.1109 65.3613 3.4493 Cloud, I-SMAC 2017, pp. 259–264, 2017. P7 42.0883 64.2955 3.3799 P8 41.8842 63.8282 3.38652 [3] J. Coloigner, R. Phlypo, A. Bush, and N. Lepore, P9 40.1754 61.4674 3.3137 “FUNCTIONAL CONNECTIVITY ANALYSIS FOR THALASSEMIA DISEASE BASED ON A P10 44.4860 66.4897 3.4872 GRAPHICAL LASSO MODEL,” 2016 IEEE 13th Int. P11 43.1622 65.4676 3.4763 Symp. Biomed. Imaging, pp. 1295–1298, 2016.
Table 2. PSO algorithm analysis value for detecting [4] R. Dey, K. Roy, D. Bhattacharjee, M. Nasipuri, anemia disease and P. Ghosh, “An automated system for measuring hematocrit level of human blood from total RBC count,” Image Input image Output image 2016 Intl. Conf. Adv. Comput. Commun. Informatics, pp. Patient Mean Standard Entropy Mean Standard Entropy 2273–2279, 2016. number value deviation value Value deviation value value value [5] A. F. M. Hani, L. Arshad, A. S. Malik, A. Jamil, P1 64.0210 20.4787 6.0172 28.8029 34.6731 1.2284 P2 63.6133 18.3164 5.7587 28.6208 33.7325 1.5904 and F. Y. B. Bin, “Haemoglobin distribution in ulcers for P3 54.7077 27.7175 6.4645 24.6387 32.2114 1.2244 healing assessment,” ICIAS 2012 - 2012 4th Int. Conf. P4 54.9288 27.8015 6.3567 23.4075 32.2613 1.5388 Intell. Adv. Syst. A Conf. World Eng. Sci. Technol. P5 49.4473 22.9169 6.0394 20.7833 27.9315 1.1230 Congr. - Conf. Proc., vol. 1, pp. 362–367, 2012. P6 48.0438 27.3127 6.1342 15.0329 28.1282 1.1407 P7 62.6516 28.5630 6.1019 25.1936 35.7645 1.5005 [6] A. F. M. Hani, L. Arshad, A. S. Malik, A. Jamil, P8 40.0324 22.1312 5.9801 17.2452 23.9535 1.1705 and F. Y. B. Bin, “Assessment of chronic ulcers using P9 54.5699 14.5620 5.7786 29.7348 28.7004 1.3920 digital imaging,” 2011 Natl. Postgrad. Conf., pp. 1–5, P10 98.5428 20.1159 5.7627 29.4896 49.0331 1.0452 P11 56.8875 22.2054 6.1891 23.7833 31.4386 1.1433 2011. [7] P. Kharazmi, H. Lui, Z. J. Wang, and T. K. Lee,
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