The authors have declared that no competing interests exist.
Alzheimer’s Disease (AD) is one amongst the overwhelming types of dementia that distresses the brain nerve cells leading to a perpetual loss in memory and creating a lot of difficulties for the family members in caretaking. The prediction of the disease at an earlier stage is a common problem. The most prevalent imaging modalities used for diagnosing AD are Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT). They can provide valuable information regarding the changes in internal and external brain regions and activities for diagnosing AD. But the relevant studies made on retina reveals that in addition to brain changes there are some variations on the retina layers of the AD patients. Therefore, the retina can be used as a biomarker for diagnosing AD. There are different techniques available for an eye examination. Most noticeable of them are Fundus Imaging and Optical Coherence Tomography (OCT). In this paper, we have focused on OCT retinal images of AD patients for the early diagnosis of AD.
Dementia is a brain disorder that leads to total degradation in memory as well as physical activities. The different types of dementia that are known today are Alzheimer’s disease, Vascular dementia, Parkinson’s disease, Dementia with Lewy bodies, Huntington’s disease, Creutzfeldt-Jakob disease, Frontotemporal dementia, Normal pressure hydrocephalus, Down syndrome dementia, Korsakoff syndrome and Posterior cortical atrophy. The
MRI, PET, and SPECT help to see the variations inside the brain for diagnosing AD. But relevant studies and investigations made on dementia of Alzheimer’s type reveal that there are variations on the internal layers of the retina, especially in the Retinal Nerve Fiber Layer (RNFL) in the earlier stage of AD. The main reason for variations in RNFL is due to the accumulation of Aβ protein, which is one of the most important hallmarks of AD. Due to the abnormal behavior of retina, there are disabilities in eyesight, defects in the retinal cortex, loss of anterior visual pathways, and dysfunction of nerves in optics and retinal ganglion cells
MMSE is a screening test that is used to find out the suspected AD subjects by interviewing the patients. Before going to any major analysis, screening is very important. After the screening process, OCT analysis has been done. In the OCT analysis, there are different steps such as segmentation, feature extraction and classification are required. Segmentation is a very important significant method in the automated computer prediction of medical pictures. Most significant and common strategies for segmentation of medical pictures are fuzzy logic; support vector machines (SVMs) and artificial neural networks (ANNs)
For the early diagnosis of AD using image analysis, there should a screening process that is to be made initially. From the laboratory tests such as blood tests or other common tests, it cannot screen the patients whether patients is having AD or not. In this scenario, different neuropsychological tests to examine the mental health and cognitive dysfunction of AD subjects has been used. It includes Rey Auditory Verbal Learning Test, Trial Making Test parts A and B, category fluency, Digit Span forward and backward, Digit Symbol Substitution Test, the Clock Drawing task and Mini Mental State examination (MMSE). In this Research MMSE for screening the subjects has been chosen. It can be used to systematically and thoroughly assess mental status. CE tool is used to find out the cognitive function of the patient with an eleven questionnaire method. MMSE really measures the five areas of mental status. The five areas are orientation, registration, attention and calculation, recall and language. In the orientation section different questions like month, year, place etc. were asked and the total score of this section is 10 points. In the registration section, examiner asks the name of different objects and gives a maximum score of 3 points. In the attention and calculation section, the examiner asks the subject to count backwards and will give 0 to 5 points. In the recall section, the examiner asks to name the previously told objects in the registration section and give 0 to 3 points. The fifth or final section is the language section in which the subject is asked some question related to vocabulary and gives 0 to 9 points. Finally, the total score or points are calculated for maximum of 30 points. If the score is above 23, the patient is assumed to have no Alzheimer’s. If the score lies between 18 to 23, the subject are having mild AD, if the score lie between 10 to 17, the subject is having moderate AD and if the score is less than 10, the subject is having severe AD. The scoring table is shown in
scores | condition |
24 – 30 | Normal |
18 – 23 | Mild dementia |
10 – 17 | Moderate dementia |
<10 | Severe Dementia |
A total of 50 subjects with the screening tool that we developed with the coordination of medical experts have been interviewed. For the selection of the subjects, some criteria such as the education are minimum 10th standard and no previous head injury records have been made. Thus from the interviewed 50 subjects, the normal male subjects are 10 and female are 16. Likewise in mild condition, male, 2 subjects and there is no female. In the moderate condition, male-11 and female-8 and in the severe condition, 2 males and one female. The comparison of male and female subjects is shown in
Also summarize the age group and the dementia condition as in
condition | age group |
normal | 45-55 |
mild | 55-65 |
moderate | 65-75 |
severe | 75-85 |
The retina of the eye can be called as a biomarker for predicting dementia of Alzheimer’s type. The connection between the retina and the brain is shown in
Optical Coherence Tomography (OCT) is a noninvasive technique that provides a cross-sectional view of internal layers of retinal regions. The images obtained through OCT are high-quality resolution pictures
Optical Coherence Tomography (OCT) is a noninvasive as well as an efficient technique that provides a cross-sectional view of internal layers of retinal regions. After the screening of the patients has been done, the diagnosing of the patient is required. In this paper, OCT imaging methods is used. Therefore the screened patients have been sent for OCT scanning to obtain the OCT images. For the diagnosis of AD using OCT images, the different steps involved are image acquisition, image segmentation, feature extraction, and classification
where
For creating the wavelet Network, we have combined the second and third equations with dimension
The above equation helps to build the structure of the Wavelet Network. Therefore this wavelet network can be called as Marr-Morlet Wavelet Networks (MMWNs). Next is to train the wavelet using Neural Networks and finally we get the segmented image
We have compared the segmentation results with Fuzzy C Means (FCM), K Means Clustering (KMC), and Region seed growing (RSG) which is shown in
Method | Accuracy | Precision | Sensitivity | Specificity |
Proposed | 99.65 | 93.77 | 93.32 | 98.82 |
FCM | 98.53 | 91.15 | 92.34 | 98.73 |
KMC | 97.83 | 81.28 | 82.94 | 97.83 |
RSG | 96.63 | 79.15 | 80.22 | 96.52 |
`After extracting the features, the most important part is the classification of OCT images. For this purpose, we have used Neural Network method of classification. Accordingly, Back Propagation (BP) and Radial Basis Function (RBF) type NN method has been used. We have compared both functions, in this case, NN using RBF produce good results. Finally, we classified the selected patients’ images with the OCT analysis technique that we developed using Marr-Morlet Wavelet Networks (MMWNs). The result obtained through CE tool is the same as that of OCT analysis.
From the above sections, it is clear that Alzheimer’s disease can be diagnosed through the retina, especially the OCT imaging technique. In order to make an expert system for predicting AD, a large database is required. The first step in diagnosing AD is to screen the patients using clinical and neuropsychological tests to know whether the patient is demented or not. After that, the AD patients have to be diagnosed with the OCT technique as instructed by the ophthalmologist. The images obtained through OCT should be taken under the same environmental conditions. The images obtained from the OCT device should be saved in an electronic format for creating the OCT database. For an expert system using OCT images for the early prediction of AD, segmentation, feature extraction, feature selection and classification of OCT images have to be done. The findings made using this analysis can indicate that the OCT method can be used for diagnosing AD in the early stages. The required databases of this paper preparation are obtained from Sree Gokulam Medical College and Research Foundation, Trivandrum. We have selected 50 patients for the study. First, we have interviewed the selected patients using the MMSE test. The patients are interviewed for a maximum score of 30 based on orientation, registration, attention and calculation, recall, and language. The score below 24 reports that the patient is having AD. From the total 50 patients, 25 are having the score less than 24, 30 are having the score greater than 23. Thus from the interviewed 50 subjects, the normal male subjects are 10 and female are 16. Likewise in mild condition, male, 2 subjects and there is no female. In the moderate condition, male-11 and female-8 and in the severe condition, 2 males and one female. After the screening section is over, the selected 24 AD patients have been sent to OCT scanning for taking the images of the retina, especially the RNFL layer for the diagnosis of AD. The scanned images are saved as a bitmap file for further processing. The size of each image was 535,974 byte; the database images employed in this paper are made free from noise or other artifacts by filtering or pre-processing stage. The OCT image is then segmented using WNs with the help of a combination of Marr-Morlet wavelet function. After segmentation, feature extraction has done and finally, classified the OCT images using NNs. We have compared our method with other techniques like Fuzzy C Means (FCM), K Means Clustering (KMC), and Region seed growing (RSG) and got better results. We have analysed the OCT images obtained from the selected 50 patients and classified that the 26 patients are normal and 24 are demented. Thus we got the same result as that of the screening process.
There are a lot of tests, drug therapies, biomarkers and neuroimaging techniques are available for the diagnosing AD. A definite diagnosis of AD can be done through autopsy. In this scenario, the OCT method can be widely used without disturbing the patient, as it is less expensive, noninvasive and easier to use. The early diagnosis of AD can lessen various ill effects and complications. Using OCT, it is possible to set a clinical follow up to carrying out the diagnosis. As the prevalence of AD is becoming a disturbance for the whole world, OCT imaging can provide better results than other modalities in terms of effort, cost and time. In this research, screening process is done to select the patients. After the screening, patients have been sent to OCT image analysis. The segmentation of OCT has been done with MMWNs and compared with other techniques and got better results. The classification of OCT is done with Neural Networks. The different advantages of OCT analysis includes such as a medical aid to doctors for diagnosing AD, support family members, has good sensitivity, specificity, high efficiency and less cost for taking OCT images for diagnosing AD than other major techniques.
The authors are thankful to Sree Gokulam Medical College and Research Foundation, Trivandrum, India for providing the necessary database of OCT images for the preparation of the paper