Compare and match images with different images?

Question

Disclaimer : i'm beginner maybe this question bad for you, i hope you understand.

I have to create skin diseases expert system using PHP programming. The point is matching two diferent image or more, and the system matching/compare images from database/files with images from user, and then give some question to the user who input the image. The question come from matching/compare result which roughly matches with image from database/file.

For example, this is images from user with Scabies skin diseases :

And then this is sample image from database/file.

Now how can i match /compare the images?

i already read this questions Image comparison - fast algorithm, Compare images to find differences, Tool to compare images on Windows, Algorithm to compare two images, Algorithm fast compare images \ matrix and article from http://www.cs.ubc.ca/~lowe/keypoints/ (SIFT keypoint detector) and http://www.cmap.polytechnique.fr/~yu/research/ASIFT/demo.html (ASIFT, SIFT, MSER) but it seem only with same picture just diferent from position take the picture.

and all of them can't help me ( or me not understand LOL ).

I don't know much about OpenCV library, whether OpenCV library can handle it?

Please..., i need your help. Thanks :).

Edit :

May this image can explain :

The problem is on step 2.

Answer 1

PHASE I

Go on Google Images and upload your image. Google has a "search for similar images" feature and will try to make a match. Likely Google will just match you to other pictures of skin or body parts. Set the upper limit of your expectations to matching Google's results in image recognition. If that upper limit is not good enough...

PHASE II

Use an expert system (maybe 3 layers deep of question & answer to classify the condition). Following is a list of skin conditions I have been working on. Of course you would need to put human-readable descriptions next to any medical terms

Acne
Cyst/cysts
Infected cyst
Non-infected cyst
Acne cyst
Epidermal cyst
Myxoid cyst
Ganglion cyst
Synovial cyst
Sebaceous cyst
Helial cyst
Auricular
Hidradenoma
Syringoma
Hidradenitis
[...] Nevi/nevis
Pigmented
Congenital
Typical
Atypical / Dysplastic
Inflamed
Irritated
[other]
[...] Carcinoma
Basal cell
Superficial
Squamous cell
In situ
Squamous cell ((what does this mean??))
Other
Melanoma
In situ
Keratosis/keratoses
Actinic
Seborrheic
Irritated
Pigmented
Warty
[...] Verruca (wart)
Common
Genital
Condylomatous
Plantar
Digital
Periungal
Filiform
Palmar
Urticaria (hives)
Generalized
Vasulittic
Contact
Vasculitis
Allergic
Leukocytoclastic
[...] Dermatitis
Seborrheic
Exematous
Eczematous
Eczematous
Eczematoid
Lichenoid
Psoriasiform
Pityriasiform
Nummular
Lichen simplex
Hypersensitivity
Dyshidrotic
Palmar-plantar
Psoriasis
Palmo
Plantar
Pustular
Erythrodermic
Hyperhydrosis
Lichen planus
Blistering disease
Pemphigoid
Pemphigus
Herpes simplex
Herpes zoster
Insect bite reaction
Lipom
Excoriations / prurigo
Tinea [...] (fungus)
Versicolor
Pedis
Unguium
Cruris
Capitis
Facilie
Corporis
Scarring
Post-funeral
Traumatic
Post-radiation
Acne
Keloid
Hypertrophic
Atrophic
Scleroderma
Localized
Systemic
Perleche
Cheilitis
Balanitis
Morphea
Atrophoderma
Vascular lesions
Pumpura
Eccliymosis
Angiomata
Pyogenic Granuloma
Telangiectasias
Varix
Port Wine Stain
Candidiasis
Impetigo lesions
Folliculitis
Furunculosis (boils)
Abscess 
[...] Ulceration
Infected 
Non-infected
Intertrigo
Abnormalities of Pigmentation
Post-inflammatory Hyperpigmenation
Hypopigmentation
DePigmentation
Vitiligo
Melasma
Chloasma
Rhiels Melanosis
Poikiloderma
Dyschromia
Pityriasis
Pityriasis Alba
Pityriasis Rosea
Rubra Pilaris
Lichenoides
Acuta (PLEVA)
Dry Skin
Asteatosis
Ichthyosis
Hyperkeratosis

Answer 2

I don't really know about the medical situation, the images were enough to make me sick. :)

However, I think you need to find the areas that does have different colors with the actual skin. So I recommend this link as a starting point. You can use "segmented particles" or "points at maxima" to figure out the count and the density or whatever of disorientations on the skin, and this might be a guide to what the sickness is. Also, you can get the color values of that points by "results" in the same link.

Answer 3

You could do morphological analysis on the hue images, distinguishing between normal skin color and unhealthily red color. That is, go into HSV space, extract the H component, threshold it, and then analyze the size and shape of the white areas using e.g. successive erosion.

However, the chances are pretty slim. You have a scale problem (i.e. you don't know how large the taken image is), you have the normal color/brightness normalization problems, and you have the additional problem of the large variations present in skin diseases.

This is a fairly hard problem, even for people who have studied image processing. If you don't have any prior experience in image processing (and if you are trying to use PHP for such a problem, you probably don't), prepare for a long learning process. Several months at least.