1. INTRODUCTION:

Palmistry is an integral and clever part of the science of Astrology to delve into the psyche of man through lines on one’s hand, which represents the activity of his unconscious mind. Medical palmistry grasps the hand texture, color, some features like proximal interphalanged joint, eponychium of middle finger and several special symbols called whorl, loop, grill and islands to assist diagnosing diseases.

Palmistry has divided the palm into nine zones known as Mounts. These nine Mounts resemble like magnetic centers and attract electric currents of brain. The Palmar lines show the path of those currents. The Bio-Medical instruments captures tiny bio potentials in the human body and validate the diseases; In the same way the palm prints help us to study the internal systems of the human body, based on which the diagnosis of diseases can be made.

Considering for example, the under developed Mount of Venus and defensive Mount of Mars are pointing out the physical strength of a person. There are six types of finger prints which support Medical Palmistry to indicate several diseases. To be precise, the ‘whorl ‘talks about the functionality of the digestive system where ‘tented arches ‘find persons who easily succumb to nervous disorder.

The three main lines [Life Line, Line of Head and Line of Heart] of the palm, several secondary lines and special symbols [Line of Mercury, Line of Mars, cross, star, grill and islands] reveal some medical facts of internal organs of human body. Similarly, the color, shape and texture of nails also express the healthy condition of a human.

The Pinkish, large and shiny nails of a person exhibit his good health condition whereas the Yellowish, vertically striped and Red and Black spotted nails show the illness of the person. In this paper, the author looks at some of the underpinning recent developments of Medical Palmistry which promises the next wave of innovations in accuracy.

2. LITERATURE REVIEW:

The hand geometry indicates the tendencies and traits of a person as well as his physical health criteria. Palmistry has two branches as given:

1. Chirognomy – Knowledge of shape of hand, Mounts, texture, quality of hand and fingers.

2. Chiromancy - Knowledge of lines.

Both the branches are equally providing their significant roles in the field of Medical Palmistry to identify the diseases. The lines are keep on changing according to the age, physical activity and other factors and so helping to diagnose the diseases eventually. By applying the knowledge of Medical Palmistry, the digital Image Processing analyzing techniques can be used to recognize the palm prints and to diagnose the diseases. Even though many researchers have been carried out in palm print recognition, we find only very less works in the field of Medical Palmistry. The following review discusses some researches attempted in Medical Palmistry to indicate various diseases.

2.1 CHROMOSOME DISORDERS:

Many genetical disorders are caused due to the changes in the chromosomes. Scientists have categorized gene diseases based on the exact number of the chromosome. Chromosomal disorders lead to abnormal changes of both physical and psychotic factors. P. Mastroiacovo et.al [1] has investigated sixteen patients with trisomy for the short arm of chromosome4 and found some changes in hand dermatoglyphics of them. They have observed increased frequency of whorls, less number of ulnar, radial loops and arches in their palm. Distal axial triradii was also found with an increase of main line index on palms. The authors have also referred dermatoglyphic pattern changes with an excess of arches for the other chromosome disorders like monosomy and trisomy18.

2.2 PRENATAL NEURO DEVELOPMENTAL DISRUPTIONS:

During prenatal development, there are asymmetries or some subtle physical aberrations that indicate the disruptions. These indicators are called as Minor Physical Anomalies, MPAs. The etiology of psychotic disorders is probably due to the prenatal insult and this statement is evidentially suggested by the experts.

David Gourion et.al [2] has tested eighteen nuclear families and evidenced that Neurological Soft Signs, NSS, is significantly familiar in both intra and inter family group transmission tests. So, more than the impact of the NSS, MPAs will be the deciding key factors to finalize the prenatal disruptions. This test also yields that MPAs like Curved fifth finger, excess palmar crease, retarded and tapered fingers, small and hyper convex finger nails and asymmetric hands are the strongest and sensitive parameters that estimate the severity of the illness. Michael T. Compton et.al [3] has assessed seventy three Schizophrenia patients, who are also suffered from other related psychotic disorders. They have concluded that genetic and environmental / developmental factors that influence changes in MPAs like unusual nail dermatoglyphics, curved fifth digit and changes in palmar crease raise the chances of occurrence of schizophrenia. They have also proved that the MPA scores are same for different genders and constant for various age groups and educational levels.

2.3 SCHIZOPHRENIA:

Schizophrenia is a complex disease. MRI shows abnormal cerebral structure in Schizophrenia. The abnormal social behaviors and less reactiveness to realityare normally characterized in Schizophrenia patients. They are also additionally found with anxiety disorders, major depressive illness and substance usage disorders. The physicians calculate MPA Minor Physical Anomalies, score to diagnose Schizophrenia.

Seth M. Weinberg et al [4] have carried out a meta-analysis on existing MPA literature to understand the relationship between MPA and Schizophrenia. They have calculated pooled effect sizes for both mean total MPA scores and regional MPA scores on hand for seventy six unique online pub-med databases. The meta-regression approach shows more regional MPAs in Schizophrenia patients.

David Gourion et.al [5] has also extended their research in Schizophrenia. They have utilized the revised Waldrop scale for forty one MPAs in Schizophrenia patients and their first-degree relatives. They have tested some facial MPAs along with hand MPAs like curved fifth finger [found in 32% patients], overlapping fingers [found in 75% patients], small finger nails [found in 27% patients] and hyper convex finger nails [found in 2.5% patients] .The authors have finalized that healthy comparison subjects will have lesser MPA score than the patients with Schizophrenia and their non-psychotic parents. Schizophrenia can be identified by some dermatoglyphic and geometrical features of hand. E. Z. Shamir et.al [6] has proved this statement by checking the photographs of palm prints of thirty eight patients and evaluated with thirteen biometric parameters applied on statistical regression. TamasTenyi et.al [7] has examined the topological profile of MPAs along with its rate in homicidal Schizophrenia patients with non- homicidal Schizophrenia patients by using Mehes scale with a list of fifty seven MPAs. It is suggested that the possibility of aberrant neurodevelopment is raised due to more MPAs in homicidal Schizophrenia AndrasHajnal et al [8] have contributed for Medical Palmistry through their study with fifty seven MPAs and twenty relatives of Schizophrenia patients. They have considered marked tapered fingers, curved fifth finger MPAs along with some face MPAs to calculate the MPA scores. They also accept a conclusion with former researches that states greater MPAs are good bio-markers of Schizophrenia.

2.4 CANCER:

Mohammed Ashraf et.al [9] has checked that the hand patterns can lead to diagnose the prostatic cancer. The finger length ratio between index and ring fingers, 2D:4D is correlated to medical and psychological conditions and a perfect indicator of Prenatal Androgen activity. With a group of twenty seven geriatric age patients, the 2D:4D ratio has been examined. The results have shown that mean length of 2D to 4D in right hands of the cancer patients is 10.03-10.54cms. Since low 2D:4D reflects higher in utero testosterone exposure, 74% of subjects with lesser 2D than 4D have Cancer.

Hopp Renato Nicolas et.al [10] has reviewed the recent research works concentrated so far in the relationship between 2D:4D ratio and cancer. According to the author, 2D:4D ratio is a bio marker correlated to Prenatal Testosterone [PT] and Prenatal Estrogen [PE] which influence a lot on susceptibility to all types of Cancers. Prostate Cancer and digit ratio correlation is discussed by two Korean, one UK and Spanish studies. A single work has tested seventy one cases and has found higher digit ratios in Female Cancers caused by HPV. An Australian research with 573 samples has revealed a fact that left hand digit ratio shows Breast Cancer. Through some evidential facts, the author has concluded that the higher right hand digit ratio ensures Oral Cancer and higher left hand digit ratio raises the possibilities of Gastric Cancer.

2.5 GENERAL DISEASES:

William J. Babler [11] has studied the quantitative differences in morphogenesis of primary epidermal ridge to the secondary epidermal ridge. He has observed sixty five human abortuses ranging in age from eleven to twenty six weeks. One of the conditions stated for sample is that the hands should show minimal shrinkage and sloughing of epidermis. The right hand from each specimen with 10% neutral buffered formalin is stained with Masson trichromatic connective tissue and then the epidermal ridges are measured with micrometer. The three dimensions of the regions [proximal, adjacent and distal to the core] are measured and the factors primary ridge depth, primary ridge width and inter ridge distance are also calculated. The rate of occurrence of secondary ridges will decide the maturation of the ridge system and fetus. M. E. Williams [12] has provided a detailed study of fingernails and proved that this analysis show the symptoms of diseases. He has divided the structure of the nail into three layers: The nail plate, nail bed and cuticle. Abnormalities of the nail frequently occur in its shape, color, softness and flexibility of free edge and growth rate. He has suggested that examining the finger nails will detect the overall vitality of a human in addition with cerebral dominance, cardio vascular function, rheumatic conditions and some dermatologic problems. The results yielded by him are tabulated as shown below:

Table 1: Prediction of diseases by the Parameters of nail.

S. no	Parameters of nail	Name of the diseases
1.	Clubbed nail	Lung cancer, Aortic aneurysm, inflammatory bowel disease
2.	Spooned nail	Iron and Protein deficiency, Raynaud’s disease
3.	Longitudinal ridges on nail	Rheumatoid arthritis, Peripheral Vascular disease, Folic Acid deficiency
4.	Nail beading	Diabetes Mellitus, Thyroid disorder, Vitamin B deficiency
5.	Pale blue lunula	Hematologic malignancy
6.	Red lunula	Trauma, Chronic pseudomonas SPP infection
7.	Brown lines on line	Breast Cancer

HardikPandit et.al [13] has discussed a digital image processing technique applied to Medical Palmistry. They have captured palm image of the patient through the digital camera and the image is enhanced further by noise detection, filtering and other necessary methods. The ROI is segmented and edges and lines are detected in palm. Specific symbols like star, grill and various symbols are extracted and given to IPAA module to be compared with saved data base to predict diseases.

Lloyd Hamilton [14] has concentrated on palmar tri-radii to diagnose diseases. Tri-radii are “Y” Shaped joining ridge, which appears in mid-way across the area at the base of fingers. The skin ridge patterns are carefully examined. The tri-radii are noted as A, B, C, D and T in a palm. The average AB ridge count is around 34-40 and ADT angle will be around 45-50◦ and average loop count is from 12-14. Based on the different values of these parameters, diagnosing is made. He has consolidated the results as:

Table 2: Prediction of diseases by Palmprints.

S.no	Name of the disease	Palm prints
1.	Down’s syndrome	10 Ulnar loops and reduced number of whorls, arches and radial loops
2.	Alzheimer	9 to 10 fingertip loops
3.	Myopia	Three or more fingertip whorls
4.	Intestinal obstruction and constipation	Three or more fingertip arches
5.	Heart diseases	More ulnar loops

Vipra Sharma et.al [15] has deduced a nail color analysis image processing pragmatic solution to extract nail area and scrutinize it to detect diseases. The texture and color of the nail are the deciding parameters, which are trained to the neural network to make decisions. They have listed out their outcomes as:

Table 3: Prediction of diseases by the color of nail.

S. No	Parameters of nail	Name of the diseases
1.	Beau nail	Myocardial infarction, Hypocalcaemia
2.	Nails pitting	Psoriasis, Alopecia areata
3.	White nail	Anemia, Renal failure, Cirrhosis
4.	Red nail	Angioma, Malnutrition, Polycythemia
5.	Yellow nail	Jaundice, Amiloidosis
6.	Green or black nail	Trauma, Chronic pseudomonas SPP infection

DishaDesai et.al [16] has proposed an Image Processing System, which attempts to diagnose diseases automatically. Input image of palm is snapped through 300dpi resolution web camera and smoothened by using Hessian based Frangi filter. Gaussian distribution is marking the whole area and [based on edge distance] distance algorithm is enhancing the image. Corr matching is finally used to detect symbols and disease prediction is done by comparing each symbol to a Boolean value. 94% accuracy rate is achieved. The proposed system predicts an output for 20 images in 300 seconds.

ShubhangiMeshram et.al [17] has applied digital image processing technique to compare patient’s palm prints with the data base and predict some probable diseases. They have concentrated on four comparison parameters such as texture, color of palm, color of nails and some special symbols present on palmar. They also have tabulated some results like:

1. Star on the Mount of Moon shows urinal diseases

2. Grill on the Mount of Venus indicates reproductive system problems.

3. Island on palmar identifies heart diseases.

NityashBajpai et.al [18] has deduced an image processing system to read human palms and nails in order to predict diseases. Input images are captured through a high definition camera and scanned by a Flat Bed Scanner. All the color images will be converted into gray scale images by average method. Using Frichen edge detection algorithm, the edges and lines are detected and Erosion and Dilation processes tend to smoothen the image. Existing PCA algorithm is applied to capture and compare the symbols to predict the diseases.

D. Thirumal et.al [19] has discussed the application of pattern recognition in health care domain to diagnose diseases. High resolution digital camera gives input to the system and noise cleaning and edge sharpening processes improve the quality of the images. Enhanced image is divided further into four quadrants to locate the Mounts. In addition with lines of palmar, some patterns like star, island and other symbols are extracted and all the patterns are successfully verified by the IPAA module to produce the output.

The following table finalizes all the collected erudite information of the research works done in the field of Medical Palmistry:

Table 4: Comparison evaluation in Medical Palmistry.

S. No	Title of the paper	Techniques	Advantages	Disadvantages
1.	Hand dermatoglyphics in Trisomy 4P	Calculated increased no of whorls and decreased no of loops and arches	Applicable to find 2-3 chromosomal disorders	Suggested for more no of patients under test
2.	NSS and MPAs in Schizophrenia: different transmission within families	Hand MPAs like curved, tapered fingers and nails are tested	Finds the severity of the illness	Small sample size
3.	NSS and MPAs in patients with Schizophrenia and related disorders, their first degree biological relatives	Influence of hand MPAs on raise of Schizophrenia is discussed	Proved that MPAs are independent of gender, age and educational level.	Homogeneous and larger sample is required
4.	MPAs in Schizophrenia: A Meta Analysis	Pooled effect sizes for mean total MPA and regional MPA areanalyzed.	Online pub-med data base has been taken as input	Real time and larger sample is needed
5.	MPAs are more common among the first degree unaffected relatives of Schizophrenia patients results with the Mehes scale	Considered tapered fingers, curved fifth digit and face MPAs to calculate MPA score	Proved hand MPAs are good bio-markers of Schizophrenia	Compared only with former researches
6.	Bio metric parameters of the hand as an index of Schizophrenia-A preliminary study	Photographs of palm prints are compared and applied in statistical regression	13 bio parameters are checked	Larger data base needed
7.	MPAs in patients with Schizophrenia and their parents: prevalence and pattern of craniofacial abnormalities	In Schizophrenia patients and first degree relatives of them, Waldrop scale is used to test 41 MPAs	Exactly measured: curved fifth finger-32%, overlapping fingers – 75%, small fingernails – 27%	Small sample size
8.	MPAs are more common in Schizophrenia patients with the history of homicide	Compared homicidal and non-homicidal Schizophrenia patients	57 MPAs and Mehes scale is used	Larger data base needed
9.	Hand patterns in prostatic cancers	Ratio of 2D to 4D is calculated	74% of subjects have cancer if their 2D<4D	Applied only for 27 patients
10.	Application of digital image processing and analysis in healthcare based on Medical Palmistry	Palm image is captured and ROI is segmented and edges and lines are detected	IPAA module is used	Preprocessing algorithm is not clearly explained
11.	Nail color and texture analysis for disease detection	The color and texture of the nail is analyzed for finding diseases	Neural network is used	Accuracy of processing algorithm is not discussed
12.	An Automated Medical Support System based on Medical Palmistry and nail color analysis	Color, texture of palm, color of nail and occurrence of special symbols are tested for identifying diseases	Complete hand geometry is used	Accuracy is not mentioned
13.	Examining finger nails when evaluating presenting symptoms in elderly patients	Complete nail structure is analyzed to predict diseases.	Special features like Abnormalities in flexibilityof free edge, growth rate of nail are also verified	Only manual process and no automation
14.	Digit ratio [2D:4D] and Cancer: what is known so far?	Impact of PT and PE on stimulating types of Cancer is discussed and all the research works related to digit ratio are reviewed.	Symptoms of Prostate , Female, Oral and Gastric Cancers are noted	Not mentioned
15.	Automated Medical Palmistry system based on image processing techniques	Web camera gives i/p. Frangi filter, distance algorithm and Corr matching are used to provide the o/p.	System predicts an o/p for 20 images in 300 seconds.	Only 94% accuracy is achieved.
16.	Automated prediction system for various health conditions by analyzing human palms and nails using image processing technique	HD camera gives i/p. average method, Frichen edge detection algorithm and PCA algorithm are extracting the final o/p	Quality of i/p image is high due to HD camera.	Accuracy is not mentioned
17.	Application of DIP and analysis in healthcare based on Medical Palmistry	Digital camera captures i/p. noise cleaning , edge sharpening and pattern recognition algorithms are providing o/p	Since digital camera is used, no additional digital processes required to quantize.	Accuracy is not mentioned

All the researches have been concentrating any of the two common methodologies. First one is the manual comparison of Palmar by naked eye or using Micrometer. The second methodology moves more towards the technical era and automates the comparison of Palmar through Machine Vision technology. The later method is so accurate and easier to handle than the former. Different algorithms have been effectively utilized by the authors in any parts of the image processing like preprocessing [filtering, segmentation and edge detection] and processing. Both Neural Network algorithms and Pattern Recognition methods are used by the writers to predict the diseases.

3. CONCLUSION:

Thus this solitude survey presented the study,which brings the bunch of researches concentrated on Medical palmistry in the author point of view. Many inquisitive and sagacity experimental and statistical resultsand theirimprovements from the past experience have also beendiscussed. In particular, wehave keenly focused at some of the recent underpinning technical developments whichpromise to enable the next wave of innovations inaccuracy and reliability of Machine Learning and predicting diseases. We hope that this survey will be definitely fruitful to the researchersfrom the same area to continue their technical search in a new dimension.

4. REFERENCES:

1. P. Mastroiacovo, V.Curro and Anna Calabro, 1976.”Hand Dermatoglyphics in Trisomy 4P”, Hum.Genet.34, Springer, 271-276.

2. David Gourion, Celine Goldberger, Marie Chantal Bourdel and Frank Jean Bayle, 2002. “Neurological Soft Signs and Minor Physical Anomalies in Schizophrenia: Different transmission within families”, Schizophrenia Research 63, Elsevier, 181-187.

3. Michael T. Compton, Annie M. Bolloni, La Tasha Mckenzie Mark and Aimee D. Kryda, 2007. “Neurological Soft Signs and Minor Physical Anomalies in patients with Schizophrenia and related disorders, their first degree biological relatives and Non-Psychiatric controls”, Schizophrenia Research 94, Elsevier, 64-73.

4. Seth M. Weinberg, Elizabeth A. Jenkins and Mary L. Marazita, 2007. “Minor Physical Anomalies in Schizophrenia: A Meta- analysis”, Schizophrenia Research 89, Elsevier, 72-85.

5. David Gourion, Celine Goldberger, Marie Chantal Bourdel and Frank Jean Bayle, 2004. “Minor Physical Anomalies in patients with Schizophrenia and their parents: prevalence and pattern of craniofacial abnormalities”, Psychiatry Research 125, Elsevier, 21-28.

6. EyalZvi Shamir, Stanley Moris Casan, Anat Levy and Tova Lifshitz, 2013. “Bio Metric parameters of the hand as an index of Schizophrenia – A Preliminary study”, Psychiatry Research 210, Elsevier, 716-720.

7. Tamas Tenyi, Tamas Halmai, Albert Antal, Balint Benke and SaraJeges, 2015. “Minor Physical Anomalies are more common Schizophrenia patients with the history of Homicide”, Psychiatry Research 225, Elsevier, 702-705.

8. Andras Hajnal, GyorgyiCsabi, Robert Herold, Sara Jeges and Tamas Halmai, 2016. “Minor Physical Anomalies are more common among the first-degree unaffected relatives of Schizophrenia patients with the Mehes Scale”, Psychiatry Research 237, Elsevier, 224-238.

9. Mohammed Ashraf, Thomas Mohan and Edmond Fernandes, 2014. “Hand Patterns in Prostatic Cancers”, Journal of Dental and Medical Sciences, volume 13, issue 3, 72-74.

10. Renato Nicolas Hopp, Nathalia Caroline Souza Lima, Jose Laurentino and Ferreira Filho, 2014. “Digit Ratio [2D:4D] and Cancer: What is known so far?, International Journal of Cancer Therapy and Oncology.

11. William J. Babler, 1978. “Qualitative differences in morphogenesis of human epidermal ridges”.

12. M. E. Williams, 2009. “Examining the fingernails when evaluating presenting symptoms in elderly patients”, www. Medscape. Com.

13. Hardik Pandit and Dr M. Shah, 2011. “Application of Digital Image Processing and analysis in healthcare based on Medical Palmistry”, International Journal of Computer Applications.

14. Lloyd Hamilton, 2009. “Palmar Tri-Radii”.

15. Vipra Sharma and Manoj Ramaiya, 2015. “Nail color and texture analysis for disease detection”, International Journal of Bio-Science and Bio-Technology, volume 7, issue 5, page 351-358.

16. Disha Desai, Mugdha Parekh and Devanshi Shah, 2015. “Automated Medical Palmistry System based on Image Processing Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, volume 5.

17. Shubhangi Meshram, Anuradha Thakare and Santwana Gudadhi, 2016. “An Automated Medical Support System based on Medical Palmistry and nail color analysis”, International Journal of Engineering and Computer Science, volume 5, issue 6, page 17006-17009.

18. Nityash Bajpai, Rohit Alavadhi and Anuradha Thakare, 2015. “Automated prediction system for various health conditions by analyzing human palms and nails using Image Matching Technique”, International journal of Scientific and Engineering Research, volume 6, issue 10.

19. D. Thirumal Reddy and P. Balaramudu, 2015. “Application of Digital Image Processing and analysis in health care based on Medical Palmistry”, International Journal for development in Computer Science and Technology.

20. Bhupendra Dholakiya, “Sampurna Hastarekha Shastra”, Uzma publication, Ahmedabad.

21. D. M. Shah, 2007, “Decision Support System for Image Analysis”, in Journal of Advanced Research in Computer Engineering, page 51-56.

Received on 25.05.2017 Modified on 18.07.2017

Research J. Pharm. and Tech 2017; 10(11): 4033-4038.

DOI: 10.5958/0974-360X.2017.00731.4