Download Progress Report on Psoriasis Diagnosis Using Image

A novel approach for diagnosis and prognosis
of skin diseases using image analysis
Student:
Guide:
Parameshwar R. Hegde
Junior Research Fellow
Dept. of Dermatology and
Yenepoya Research Centre
Yenepoya University
Mangalore
Dr. Manjunath Shenoy
Professor and H.O.D.
Dept. of Dermatology
Yenepoya Medical College
Yenepoya University
Mangalore
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Contents
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Background
Introduction
Literature survey
Rational of study
Aim and objectives
Relevance of study
Materials
Methods
Timeline
Budget
Social relevance
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Background for study
• Skin diseases are important health problems which also
impair the quality of life (Kurpiewska et al., 2011)
• The lesions seen in skin diseases may vary in severity from
minor localized patches to complete body coverage (Ho, 2010)
• Diagnosis mainly depends on the experience of the clinician
• Human judgement may have discrepancies in quantification
of information gathering, accuracy, reproducibility of data
(Broek et al., 2011)
• Biopsy for histopathology is, invasive test and also time
consuming (Manocha et al., 2011)
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Introduction
• Image analysis is the extraction of relevant information from
images using the digital image processing techniques
(Gonzalez and Woods, 2002)
• It is very closely related to the medical imaging field, medical
image analysis focuses on the computational analysis
• The methods can be grouped into several categories: image
acquisition, image segmentation, feature extraction (Gonzalez
and Woods, 2002)
• Machine learning draws results from artificial intelligence,
probability and statistics, control theory (Mitchell, 1997)
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Literature survey
Table 1. International scenario of the study.
Reference
Giotis et al.,
2015
Cheerla and
Frazier, 2014
Data examined
Melanoma/
Naevus
Sample
size
70/ 100
Feature extracted Accuracy (in
%)
Color (HSV)
Meligant
100/ 100 Texture (ABCD rule
melanoma/ Benign
and Wavelet
melanoma
transformation)
Hashim et al., Plaque/ Guttate/
30/ 30/ 30 Texture (Wavelet
2013
Erythroderma
transformation)
Egmal, 2013 Melanoma/ Normal 20/ 20
Texture (Wavelet
skin
transformation)
Arifin et al.,
Acne/ Eczema/
107/ 102/
Texture (GLCM)
2012
Psoriasis/ Tinea
105/ 107/
Corporis/ Scabies/ 182/ 101
Vitiligo
81
95.4
80
97.5
94
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Literature survey
Table 2. National scenario of the study.
Reference
Data examined
Shrivastava et Psoriasis/ Normal
al., 2015
skin
Sample
size
Feature extracted Accuracy (in
%)
270/ 270
Texture (GLCM),
Color (RGB, HSV)
99.81
Texture (Gabor
filter)
87
Kavitha and
Saravanan,
2014
Kumar and
Reddy, 2014
Psoriasis/
Normal skin
Suvarna et al.,
2013
Jaleel et al.,
2012
Skin burn (in 3
40/ 40/ 40
grades)
Melanoma/ Normal 100
skin
Skin burn (in 2
grades)
103
49/ 49
Color segmentation 81
Color (HSV)
95
Texture
distinctiveness
98.4
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Rationale of study
• The available information related to current work is scarce
• Currently 15 working online databases are available
worldwide (Dermnet, 1998; Hardin.MD, 2010) and no
authentic online databases are available from India
• Existing databases has limitations like, updation, diseased
image metadata, user registration, prognosis tool
• Earlier disease classification tools, classified specific disease
from normal skin and other 6 related skin diseases
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Relevance of study
• Teledermatology services will be easily instituted for patients
with skin ailments
• Assist the dermatologists in disease prognosis
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Aim of study
• To develop a teledermatology application including database
and software to compare and classify various skin diseases
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Objectives of study
• To collect the diseased image and create a database
• Use the database for teledermatology application
• To develop a classification tool for skin diseases
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Materials
• A camera to collect the images (Canon 1200D with 18-55 and
50mm lens)
• A high performance computer (1TB HDD, 8GB RAM, i7
processor, 2GB graphic card, 18.5” TFT) for programming
• MATLAB tool to develop the machine learning based diagnosis
software
• Server to host teledermatology application
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Methods
• Data (image) collection
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White or dark background using digital camera
Sample size – 1000 images (Arfin et al., 2012, Yusof et al.,
2011)
Sample – Normal skin, Diseased lesion (Plaque psoriasis,
Chronic eczema, Lichen planus, Pityriasis rosea)
Sample source – Department of skin and VD, Yenepoya
medical college, Yenepoya University
Naming each image with a unique code and create a
database in MS Excel
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Methods
• Teledermatology application
Creating application backend by collected information
 Backend – MySQL
 Developing graphical interface for user
 Frontend – PHP, Java Scripts
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• Diagnosis software
Pre-processing – Removing unwanted information from
image
 Hair removing, cropping
 Feature extraction – Extracting useful regions from image
 Color and texture features
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Methods
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Classification – Making a decision based on extracted
feature
• Statistical analysis
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Sensitivity, specificity and accuracy
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Time line
Work
6
months
12
months
18
months
24
months
30
months
36
months
Literature review
Image collection
Knowledge base
design
Image analysis
System testing
Publication and
report submission
Figure 1. Work plan (Gantt chart) of the proposed study.
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Budget plan
Sl. No. Requirement
Justification
Quantity
Amount
1
To collect the images from
1
30,000
1
58,450
1
3,00,000
1
53,000
Camera
patients (Canon 1200D with 18-55
and 50mm lens)
2
Computer
To analyze images, classify and
develop the application
(1TB HDD, 8GB RAM, i7 processor,
2GB graphic card, 18.5” TFT)
3
MATLAB
Original tool required to build the
software
classification software package
(Multi-installation)
4
Linux server
To host the application and
mentainance
Total
4,41,450
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References
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Arifin, ML., Kibria, MG., Firoze, A., Amin, MA., Yan, H. (2012). Dermatological
disease diagnosis using color skin images. International Conference on Machine
Learning and Cybernetics, Xain. p. 1675-1680.
Broek., Harris, N., Henkens, M., palma, PP., Szumilin, E. (2013). Introduction. In:
Giouzard V, editor. Clinical guidelines: Diagnosis and treatment manual.
Medicines Sans Frontieres. pp. 10-12.
Cheerla, N., Frazeir, D. (2014). Automatic melanoma detection using multi-stage
neural networks. International Journal of Innovative Research in Science,
Engineering and Technology 3:9164-9183.
New Zealand Dermatological Society (1998). Dermnet, viewed 14 June 2016,
http://www.dermnet.com/dermatology-pictures-skin-disease-pictures/.
Egmal, M. (2013). Automatic skin cancer images classification. International
Journal of Advanced Computer Science and Applications 4:287-294.
Giotis, I., Molders, N., Land, S., Biehl, M., Jonkman, MF., Petlov, N. (2015). MEDNODE: A computer assisted melanoma diagnosis system using non-dermoscopic
images. Expert Systems with Application 42:6578-6585.
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References
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Gonzalez, RC., Woods, RE. (2002). Introduction. Digital image processing. Pearson
Educational International. pp. 24-29.
University of Iowa (2010). Hardin.MD, viewed 14 June 2016,
http://hardinmd.lib.uiowa.edu/index.html.
Hashim, H., Ramli, S., Wahid, N., Hassan, N. (2013). Recognition of psoriasis
features via Daubechies D8 wavelet technique. International journal on smart
sensing and intelligent systems 6.
Ho, KM. (2010). Psoriasis. Medical bulletin 15:10-14.
Jaleel, JA., Salim, S., Aswin, RB. (2012). Artificial neural network based detection
of skin cancer. International Journal of Advanced Research in Electrical,
Electronics and Instrumentation Engineering 3:200-205.
Kavitha, J., Saravanan, D. (2014). Localizing 2-D digital skin images using savc
algorithm. International Journal of Innovative Research in Science, Engineering
and Technology 3.
Kumar, KS., Reddy, BE. (2014). Wound image analysis using contour evolution. I.J.
Image, Graphics and Signal Processing 6:36-42.
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References
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Kurpiewska, J., Liwkowicz, J., Benczek, K. (2011). A survey of work-related skin
diseases in different occupations in Poland. International Journal of Occupational
Safety and Ergonomics 17:207-214.
Mitchell, T. (1997). Introduction. In: Book news, editors. Machine Learning.
Morgan Kaufmann. pp. 1-6.
Manocha, D., Bansal, N., Farah, RS. (2011). Types and selection criteria for various
skin biopsy procedures. In: Dr. Khopkar U, editors. Skin biopsy – perspectives.
INTECH Open Access Publisher. pp. 35-39.
Shrivastava, VK., Londhe, ND., Sonawane, RS., Suri, JS. (2015). Reliable and
accurate psoriasis disease classification in dermatology images using
comprehensive feature space in machine learning paradigm. Expert Systems with
Applications 42:6184-6195.
Suvarna, M., Sivakumar., Kumar, K., Niranjan, UC. (2013). Diagnosis of burn
images using template matching, k-nearest neighbor and artificial neural
network. International Journal of Image Processing 2:109-226.
Yusof, YW., Hashim, H., Sulaiman, KA. (2011). Plaque lesion classification fuzzy
model based on various color models. The Third International Conference on 19
Advances in System Simulation, Barcelona. p. 88-93.
Thank You
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