IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Mohammed Asaduzzaman: Mitigation in Bangladesh's National Climate Change Action Plan and priorities for research (presentation from Mitigation session at CCAFS Science Workshop, December 2010)
Skin Lesion Segmentation Using TDLS Algorithm and Pattern Identificationpaperpublications3
Abstract: The rapid growing rate of Melanoma makes researchers to study in depth about it. It is the most deadliest form of skin cancer. Due to the higher costs for dermatologists, images of skin lesion of the patient are taken and texture-based skin lesion segmentation algorithm is proposed. Different regions belonging to different textures are studied. Finally the image is classified as normal skin or lesion. Different model based pattern classification of the lesion is proposed. We mainly classify the whole pigmented lesion into three possible patterns: globular, reticular, and homogeneous.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Mohammed Asaduzzaman: Mitigation in Bangladesh's National Climate Change Action Plan and priorities for research (presentation from Mitigation session at CCAFS Science Workshop, December 2010)
Skin Lesion Segmentation Using TDLS Algorithm and Pattern Identificationpaperpublications3
Abstract: The rapid growing rate of Melanoma makes researchers to study in depth about it. It is the most deadliest form of skin cancer. Due to the higher costs for dermatologists, images of skin lesion of the patient are taken and texture-based skin lesion segmentation algorithm is proposed. Different regions belonging to different textures are studied. Finally the image is classified as normal skin or lesion. Different model based pattern classification of the lesion is proposed. We mainly classify the whole pigmented lesion into three possible patterns: globular, reticular, and homogeneous.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
Skin Cancer Prognosis Based Pigment ProcessingCSCJournals
This paper develops a new computerized vision of skin cancer prognosis based on symmetry and color matching for lesion pigments. Initially, the lesion/tumor edge is detected and segmented. Then, the symmetrization is computed for all images to isolate benign (mole) tumor. The even symmetry parameter is introduced here to improve the symmetrization computations. The suspicious images would be nominated into one of three categories: melanoma, Basal Cell Carcinoma (BCC), or Squamous Cell Carcinoma (SCC) tumor depending on the symmetrization and pigment-color matching score table. Two matching procedures have been developed for nominating the suspicious images. First procedure matches pigment values with artificial spectrums of Reddish, Yellowish, Brownish, and Blackish. The second procedure matches pigment values with true malignancy/benign pigment database. The results of two procedures are compared over 40 pre-classified images. With Mean Squared Error (MSE) value equals to 0.003, procedure#1 satisfied 80% true classification while 92.5% for procedure#2. These results could be improved if lesion segmentation and/or spectrums/pigment-database are increased.
In recent days, skin cancer is seen as one of the most Hazardous form of the Cancers found in
Humans. Skin cancer is found in various types such as Melanoma, Basal and Squamous cell Carcinoma among
which Melanoma is the most unpredictable. The detection of Melanoma cancer in early stage can be helpful to
cure it. Computer vision can play important role in Medical Image Diagnosis and it has been proved by many
existing systems. In this paper, we present a survey on different steps which are being to detect the Melanoma
Skin Cancer using Image Processing tools. In every step, what are the different methods are be included in our
paper.
large data set is not available for some disease such as Brain Tumor. This and part2 presentation shows how to find "Actionable solution from a difficult cancer dataset
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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Detection of erythemato-squamous diseases using AR-CatfishBPSO-KSVMsipij
Nowadays, one of the most important usages of machine learning is diagnosis of diverse diseases. In this work, we introduces a diagnosis model based on Catfish binary particle swarm optimization (CatfishBPSO), kernelized support vector machines (KSVM) and association rules (AR) as our feature selection method to diagnose erythemato-squamous diseases. The proposed model consisted of two stages. In the first stage, AR is used to select the optimal feature subset from the original feature set. Next, based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy and CatfishBPSO is a promising tool for global searching, a CatfishBPSO based approach is employed for parameter determination of KSVM. Experimental results show that the proposed AR-CatfishBPSO-KSVM model achieves 99.09% classification accuracy using 24 features of the erythemato-squamous disease dataset which shows that our proposed method is more accurate compared to other popular methods in this literature like Support vector machines and AR-MLP (association rules - multilayer perceptron). It should be mentioned that we took our dataset from University of California Irvine machine learning database.
Maxillofacial Pathology Detection Using an Extended a Contrario Approach Comb...sipij
In this work, we develop a method for pathology detection of the maxillary-facial in 3D CT medical images. Our method is based on “a contrario” approach that is a statistical region based method. To do this, we detect significant changes in regions previously delineated by a physician anatomist. We apply a decision algorithm based on fuzzy logic on the changes detected and we get a decision with a degree of uncertainty. Tests conducted on a basis of real images have shown the performance and robustness of the proposed approach.
Brain Tumor Segmentation and Volume Estimation from T1-Contrasted and T2 MRIsCSCJournals
Amid the variations of the cancer disease, brain tumors account for the majority deaths among young people. To diagnose and treat this deadly disease effectively, analysis of hundreds of medical images such as Magnetic Resonance Imaging (MRI) scans is usually performed. However, the analyses of these scans are still mainly performed manually, making the procedure not only very tedious and time-consuming for doctors, but also error prone and non-repeatable. Attempts have been made to automate this procedure by performing image processing techniques such as thresholding, region-growing, unsupervised learning (e.g. k-means, fuzzy c-means clustering), and supervised learning (e.g. support vector machines). Some require human interaction. The techniques may be applied on one or more MRI sequence scans. Unfortunately, these automated attempts still result in a high level of error, and more computationally complex algorithms do not guarantee an increase in accuracy. This paper presents a novel, fully automatic brain tumor segmentation and volume estimation method using simple techniques on T1-contrasted and T2 MRIs. This new approach implemented five main steps: preprocessing using anisotropic diffusion, segmentation of tumor regions using k-means clustering, region combination using logical and Morphological operations, error checking using temporal smoothing, and volumetric measurement. When compared with five state-of-the-art algorithms, the proposed algorithm outperformed those in past works. Advances were seen by its noise reduction, increase in accuracy and closeness to actual tumor volume.
A deep convolutional structure-based approach for accurate recognition of ski...IJECEIAES
One-third of all cancer diagnoses worldwide are skin malignancies. One of the most common tumors, skin cancer can develop from a variety of dermatological conditions and is subdivided into different categories based on its textile, color, body, and other morphological characteristics. The most effective strategy to lower the mortality rate of melanoma is early identification because skin cancer incidence has been on the rise recently. In order to categorize dermoscopy images into the four diagnosis classifications of melanoma, benign, malignant, and human against machine (HAM) not melanoma, this research suggests a computer-aided diagnosis (CAD) system. Experimental results show that the suggested approach enabled 97.25% classification accuracy. In order to automate the identification of skin cancer and expedite the diagnosis process in order to save a life, the proposed technique offers a less complex and cutting-edge framework.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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The Art of the Pitch: WordPress Relationships and Sales
Lx2520682075
1. Dr. R. Jahir Hussain, V. Shanthoshini Deviha, P. Rengarajan / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.2068-2075
Analysing The Invasiveness Of Skin Cancer Using Fractals
Dr. R. Jahir Hussain*, V. Shanthoshini Deviha**, P. Rengarajan***
*(Associate Professor in Mathematics, Jamal Mohamed College, Trichy, Tamilnadu)
**(Research Scholar, PG & Research Department of Mathematics, Jamal Mohamed College,Trichy, Tamilnadu)
***
(Assistant Professor in Mathematics, New Ideal College of Education, Trichy, Tamilnadu)
ABSTRACT
Skin cancer, manifests itself as a dark dimension. The images were dilated with circles of
lesion, most often with an irregular boundary. increasing diameter. As an approximation for a
We model the skin cancer using fractals. The circle we once again used the boxes with pixel sizes
growth of the moles can be modeled by using of 1 1, 3 3, 5 5,...,17 17 the corresponding
fractals and its dimension can be calculated, approximated radius r in pixels was calculated by
r A /
through which we can detect the invasiveness of 1/ 2
the cancer cells in an accurate manner. A new
algorithm explains the spreading of cancer in the where A denotes the area in pixels. The
tissue. The Box Counting method and the slope kS of the regression line of the double
Sausage method are used to find out the logarithmic plot of the counted pixels with respect
dimensions of the affected cells in an accurate to the radii give
manner. This fractal approach led to very DS 2 k S
promising results which improved the analysis of
skin cancer. The estimation of skin cancer has The estimated fractal capacity dimension is D S.
been analysed through the statistical test which Skin cancer is a disease in which skin cells
provides accurate results through which we can lose the ability to divide and grow normally.
find the significance of the skin lesions. Healthy skin cells normally divide in an orderly way
to replace dead cells and grow new skin. Abnormal
cells can grow out of control and form a mass or
Keywords: Box-Counting method, Fractals,
'tumor'. When abnormal cells originate in the skin,
Malignant Melanoma, Sausage method.
the mass is called a skin tumor.
I. INTRODUCTION
A fractal is "a rough or fragmented
geometric shape that can be subdivided into various
parts, each of which is (at least approximately) a
reduced-size copy of the whole". A fractal is an
object which appears self-similar under varying
degrees of magnification, and also an object with its
own fractal dimension. Fractals themselves have
their own dimension, which is usually a non-integer
dimension that greater than their topological
dimension DT and less than their Euclidean
dimension DE. Self-similarity is the major
characteristic of the fractal objects [5].Fractal
objects and process are therefore said to display Figure-1. Malignant Melanoma
„self-invariant‟ (self-similar or self-affine) Melanoma is a malignant tumor of
properties [7]. Fractal structures do not have single melanocytes. Melanoma is sometimes known as
length scale, while fractal process (time-series) Cutaneous melanoma or Malignant melanoma. The
cannot be characterized by a single-time scale. A tumor initially starts from the upper skin layer
suspected fractal object is examined by the box (epidermis) and later invades the dermis below. Fig
counting dimension. Let F be any non-empty 1.
bounded subset of Rn and let N F be the smallest Malignant melanoma can be characterized
using some physical features such as shape, edge,
number of sets of diameter almost which can
color and surface texture. The border irregularity of
cover F, the box dimension of F
pigmented skin lesions was identified by Keefe et al.
log N F
dim B F lim [3] as the most significant diagnostic factor in
0 log clinical diagnosis of melanoma. Research by Morris
Smith [6] revealed that “irregularity” is one of the
Sausage method or boundary dilation major vocabulary terms used for describing border
method is very closely related to the Minkowski of the malignant melanoma in medical textbooks.
2068 | P a g e
2. Dr. R. Jahir Hussain, V. Shanthoshini Deviha, P. Rengarajan / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.2068-2075
These findings coupled with the fact that border n-1) and connect each of the 2n bottom nodes of
irregularity is one of the major features in the seven these two units to root of the network.
point checklist used for computing a “suspicious- The above algorithm has been programmed and run
ness” score for skin lesions [5] indicate that border by using C++.
irregularity is a very important factor in the
diagnosis of malignant melanoma.
Several recent research studies have
investigated early detection method using computer
images analysis techniques. The previous article
deals histologically with indentation and protrusion
also the lesion border suggested the regression of a
melanoma or excess cell growth.[4] In this Paper,
we pointed out the structural dimension using
Sausage method (Capacity dimension) and the
overall dimension can be obtained by box-counting
method (box-counting dimension) which is found
out for few samples.
II. METHODOLOGIES
2.1 CIRCULATION METHOD
The circulation method is commonly
modeled in a simple random way, which in one time
unit advances one step of length a to a randomly Figure-2. Hierarchical model for cell growth
chosen nearest-neighbor site on a given d-
dimensional lattice. Assume that a random process 2.2 PERCOLATION MODEL
starts at time t 0 at the origin of the lattice. A single percolation cluster is generated in
After„t‟ time steps, the actual position of the process the way of the cancer spreading in the organ. In a
is described by the vector square lattice, each site represents an individual
t which can be infected with probability (p) and
r t a e which is immune with probability (1-p). At an initial
1 time t=0 the individual at the centre of the lattice
where e denotes the unit vector pointing (cell) is infected. We assume now that in one unit of
time this infected site infects all non-immune
in the direction of the jump at the th times. The nearest neighbor sites. In the second unit of time,
above process can be explained through power law these infected sites will infect all their non immune
exponential. So, this is the main parameter for nearest neighbor sites, and so on. In this way, after t
finding out the cells dimension. The construction of th
the model that follows a hierarchical rule is shown time steps all non immune sites of the l square
in the Fig 2. The network is built in an iterative grid around the cells are infected, i.e. the maximum
fashion, the iteration is repeating and reusing the length of the shortest path between the infected sites
cells generated as follows [8]: and the cell is l t . This process of randomly
infecting individuals is exactly the same as that of
2.1.1 ALGORITHM randomly occupying sites in the site percolation,
Step 1: Start form a single node, and then designate where the sites of a lattice have been occupied
that as the root of the graph. randomly, when the bonds between the sites are
Step 2: Add two more nodes and connect each of randomly occupied, we speak of bond percolation.
them to the root. So far, we considered on either site or bond
Step 3: Add two points of three nodes, each unit percolation, where either sites or bonds of a given
identical to the network created in the previous lattice have been chosen randomly. This site bond
iteration (step2), and then connect each of the percolation can be relevant for the spreading of the
bottom nodes (see figure) of these two units to the cancer in the tissue.
root. That is, the root will gain four more new links.
Step 4: Add two units of nine nodes each, identical 2.2.1 ALGORITHM
to the units generated in the previous iteration, and Step 1: The origin of an empty lattice is occupied.
connect all eight bottom nodes of the two new units Step 2: The nearest neighbor sites from the origin
to the root. These rules can be easily generalized. are either occupied with probability (p) or blocked
Indeed, step n would involve the following with probability (1-p).
operation. Step 3: The empty nearest neighbors sites proceed
Step n : Add two units 3n-1 of nodes each, identical as step 2 with probability (p) and blocked with
to the network created in the previous iteration (step probability (1-p).This process continues until no
sites are available for growth. Thus percolation
2069 | P a g e
3. Dr. R. Jahir Hussain, V. Shanthoshini Deviha, P. Rengarajan / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.2068-2075
clusters are generated with a distribution of cluster out by Form Factor and Invaslog. The above factors
size s. i.e, sns (p). The factor s comes from the site have been calculated for the original image as well
as dermatologist image. The statistical report also
of the cluster and has the same chance of being the provides the level of significance through which we
origin of the cluster, and thus exactly the same can analyse the invasiveness of the cancer cells.
cluster can be generated in s ways, enlarging the
distribution sns (p) by a factor of s. (Fig-3)
2 1 2 3
0 1 2
2 3
3
Figure-3. First four steps for the percolation
model
The above method is particularly useful for
studying the structure and physical properties of Figure -4. Original Image
single percolation cluster. From this the irregular
border of the cancer is formed. The above algorithm
has been programmed and run by using C++ [1].
2.3 DISTANCE MEASURE
Distance measure is the distance from the
centre of mass to the perimeter point xi , y i . So
the radial distance is defined as
d (i) x(i) x 2 y(i) y 2
i=0, 1,2,…… .N-1
Here d i is a vector obtained by the distance
measure of the boundary pixels. A normalized
vector r i is obtained by dividing d i by the
maximum value of d i [2].
The quantitative parameters such as Area, Figure -5. Dermatologist Image
Perimeter, Formfactor and Invaslog can found out
using Sausage method. Perimeter stands for the sum
of all individual perimeters of all individual objects
in the image and was calculated correspondingly.
Formfactor 4 Area perimeter 2
Invas log log( Formfactor )
The value of Invaslog has been specially
proven to be a strong quantitative measure for the
invasiveness in the skin cancer.
III. RESULTS BASED ON SKIN LESION
IMAGES
The dimension D of the cancer cell images
have been estimated using Box-counting Method
D B and Sausage Method Ds .The amount of
invasiveness of the cancer cells in the skin are found
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4. Dr. R. Jahir Hussain, V. Shanthoshini Deviha, P. Rengarajan / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.2068-2075
SKIN LESION 2 (ATYPICAL)
Table -1. Box-Counting Method
Original image Dermatologist
Scaling
Perim
Invasl
Perim
Invasl
factor
factor
Form
Form
Total
Total
Dime
nsion
Dime
nsion
Area
Area
area
area
eter
eter
Db
Db
og
og
2 6783 488 7271 0.358 0.446 1757 277 2034 0.288 0.541
3 2923 389 3312 0.243 0.614 738 197 935 0.239 0.622
4 1612 293 1905 0.235 0.629 400 140 540 0.256 0.592
5 1009 231.6 1240.6 0.236 0.627 245 108 353 0.264 0.578
6 683 194.8333 877.8333 0.226 0.646 1.9 165 88 253 0.268 0.572 1.9078
7 490 170 660 0.213 0.672 118 71 189 0.294 0.532
8 376 137 513 0.252 0.599 88 59 147 0.318 0.498
9 283 128.8889 411.8889 0.214 0.670 68 46 114 0.404 0.394
10 229 107.3 336.3 0.250 0.602 53 40 93 0.416 0.381
Table-2. Sausage method
Scaling Original image Dermatologist
Area Radius Ks Ds Area Radius Ks Ds
2 Ks 2 Ks
3 2923 30.503 738 15.327
5 1009 17.921 245 8.831
7 490 12.489 118 6.129
9 283 9.491 0.79 1.21 68 4.652 0.48 1.52
11 185 7.674 42 3.656
13 127 6.358 28 2.985
15 92 5.412 19 2.459
17 68 4.652 14 2.111
SKIN LESION 13 (BENIGN)
Table-3. Box-Counting Method
Original image Dermatologist
Scaling
Dimen
Dimen
Perim
Invasl
Perim
Invasl
factor
factor
Form
Form
Total
Total
Area
Area
area
area
sion
sion
eter
eter
Db
Db
og
og
2 4722 1167 5889 0.044 1.357 1189 451 1640 0.073 1.137
3 1935 852.3342 2787.3342 0.033 1.482 454 322 776 0.055 1.260
4 1036 585.5 1624.5 0.038 1.420 228 228 456 0.035 1.456
5 620 457.0002 1077.0002 0.037 1.432 134 162 296 0.064 1.194
6 412 346.2224 758.2224 0.433 1.37 1.9 83 131 214 0.061 1.215 1.9
7 283 283.9389 566.9389 0.044 1.357 55 105 106 0.063 1.201
8 209 229.4688 438.4688 0.050 1.301 40 84 124 0.071 1.149
9 160 185.8766 345.8766 0.058 1.237 29 71 100 0.072 1.143
10 114 170.9801 284.9801 0.049 1.310 22 61 83 0.074 1.131
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5. Dr. R. Jahir Hussain, V. Shanthoshini Deviha, P. Rengarajan / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.2068-2075
Table-4.Sausage method
Scaling Original image Dermatologist
Area Radius Ks Ds Area Radius Ks Ds
2 Ks 2 Ks
3 1935 24.818 454 12.021
5 620 14.048 134 6.531
7 283 9.491 55 4.184
9 160 7.136 0.63 1.37 29 3.038 0.29 1.71
11 94 5.470 16 2.257
13 54 4.146 10 1.784
15 37 3.432 8 1.596
17 22 2.646 4 1.128
SKIN LESION 20 (MALIGNANT MELANOMA)
Table-5. Box-Counting Method
Original image Dermatologist
Scaling
Dimen
Dimen
Perim
Invasl
Perim
Invasl
factor
factor
Form
Form
Total
Total
Area
Area
area
area
sion
sion
eter
eter
Db
Db
og
og
2 5065 1993.5 7058.5 0.016 1.80 1130.5 545.5 1676 0.048 1.319
3 2036.6669 1478.6671 3515.3340 0.012 1.92 461 350.0001 811.0001 0.047 1.328
4 1084.6250 1023.0 2107.6250 0.013 1.8 234 252.75 486.75 0.046 1.337
5 658.0400 763.8004 1421.8403 0.014 1.85 137.2 189.6 326.8 0.048 1.319
6 430.8333 588.8336 1019.6670 0.015 1.82 1.8 85 150.9445 235.9445 0.047 1.328 1.8
7 297 477.5714 774.5714 0.016 1.80 56 123.1632 179.1632 0.046 1.337
8 228 362.7188 590.7188 0.022 1.66 44 95.3750 139.3750 0.060 1.222
9 162.0741 324.5556 486.6296 0.019 1.72 27 89.3457 116.3457 0.043 1.367
10 122.20 282.9602 405.1602 0.019 1.72 20 72.28 92.28 0.048 1.319
Table-6. Sausage method
Scaling Original image Dermatologist
Area Radius Ks Ds Area Radius Ks Ds
2 Ks 2 Ks
3 2036.6669 25.462 461 12.114
5 658.0400 14.473 137.2 6.608
7 297 9.723 56 4.222
9 162.0741 7.183 0.65 1.350 27 2.932 0.19 1.81
11 95 5.499 9 1.693
13 62 4.442 6 1.382
15 38.0267 3.479 2 0.801
17 28 2.985 3 0.977
2072 | P a g e
6. Dr. R. Jahir Hussain, V. Shanthoshini Deviha, P. Rengarajan / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.2068-2075
2073 | P a g e
7. Dr. R. Jahir Hussain, V. Shanthoshini Deviha, P. Rengarajan / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.2068-2075
2074 | P a g e
8. Dr. R. Jahir Hussain, V. Shanthoshini Deviha, P. Rengarajan / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.2068-2075
IV. CONCLUSION
In our proposed model, a new measure of
border irregularity for pigmented skin lesions based
on the cell potential has been proposed. The Box-
counting method and Sausage method are analyzed
for the original image as well as the image given by
the dermatologist. The comparison between the two
methods for the original image as well as the
dermatologist image shows us the Sausage method
gives the affordable results. The Box Counting
method gives the maximum boundary value of the
irregular border of the original image. As the
dimension increases the invasiveness of the cancer
cells also increases which can be found by sausage
method and from this results we can analyse the
invasiveness of the affected cells easily.
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