Biometrics: Implementation and Challenges & Computer-aided Diagnosis (CAD) system: An Interface between Engineering and Medical Science By Dr.G.R.Sinha, Senior grade IEEE Member Professor (Electronics & Tele. ) & Associate Director Faculty of Engineering & Technology Shri Shankaracharya Technical campus, Bhilai, INDIA Email: drgrsinha@ieee.org, drgrsinha@ssgi.edu.in

Outline Biometrics: Implementation and Challenges Introduction, Types, Traits, Challenges and scope of Research CAD: An Interface between Engineering and Medical Science Introduction, Need of CAD, Applications of CAD, CAD metrics, Breast Cancer Detection Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Inspiring Thought Take up one idea. Make that one idea your life. Think of it, dream of it. Live on that idea. Let the brain, muscles, nerves, every part of the body be full of that idea. This the way to “SUCCESS”. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Innovative & Inspiring Equation E =mc2 m = Motivation c = Commitment E = Excellence Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Research Flow Diagram Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Biometrics: Implementation and Challenges Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Simple example of biometrics Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Security Related Questions Is he really who he claims to be? Is this person authorized to use this facility? Is he in the watch list posted by the government? Answer: Use Efficient Biometric Security Technology “Biometrics is a science of recognizing an individual based on his or her physical or behavioral traits” Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Biometric Traits Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Examples Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

A Biometric System Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

System Architecture Database Template Repository Acquire & Digitize Biometric Data Extract High Quality Biometric Features Rep. Template Database Template Repository Matcher Extract High Quality Biometric Features Decision Output Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Implemented in two ways Types of Biometrics Unimodal: Deals with single trait. Multimodal: Involves multiple modalities. Implemented in two ways Verification (1:1) Identification (1:N) Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Verification (1:1) Match? YES NO Person Image Template Scan Encode 0110100100010010 Match? YES NO 0110100100010010 Are you who you claim to be? 01101 01000 10010 ID token Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Identification (1:N) Match? YES (found) Are you in our database? Person Image Template Scan Encode 0110100100010010 Match? YES (found) NO (not found) 0110100100010010 Database Are you in our database? 0101011000110010 0110100100010010 0110100100010010 Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Face Location and shape of facial attributes: Eyes, eyebrows, nose, lips etc. Limitations: 1. Improper pose and Illumination 2. Template update problem Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Fingerprint Pattern of ridges and valleys on fingertip Limitation: Large computation Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Iris Iris, is bounded by pupil and the white portion of eye Each Iris is believed to be distinctive Limitations: Considerable user participation Expensive Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Signature The way a person signs Accepted all throughout the world Limitations: Professional forgers may reproduce signatures Possibility of changing in emotional and physical conditions Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Voice Physical + behavioral biometric Depends on: Shape and size of vocal tracts, mouth, nasal cavities and lips Limitations: Behavioral part changes from age, emotional state and medical conditions (cold) Background noise Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Comparative Biometric Market Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Comparison http://www.wileyindia.com/biometrics-concepts-and-applications.html Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

ISL Biometrics http://www.wileyindia.com/biometrics-concepts-and-applications.html Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Challenges in Biometrics Large intra-class variability Noisy and distorted images System performance (error rate, speed etc.) Attacks on the biometric system Every biometric characteristic has some limitations Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Threats to Biometrics The Modern Burglar Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Imperfect Images Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Human Characteristics Universality - Everyone should have this trait/feature Uniqueness - No two persons should share the same trait Collectability – The trait can be measured Permanence – Should display low variance with time Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Vulnerabilities Override Final Decision Y/N Stored Templates Modify Template Override Feature Extractor Intercept the channel Sensor Feature Extractor Matcher Y/N Application Device Synthesized Feature Vector Override Final Decision Replay Old Data Fake Biometric Override Matcher Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Multimodal Biometrics Multimodal biometrics improves performance A combination of uncorrelated modalities (e.g. fingerprint, iris and face etc.) is expected to result in improvement in performance than a combination of correlated modalities. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Feature Level Score Level Decision Level Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Architecture of Multimodal Biometrics http://www.wileyindia.com/biometrics-concepts-and-applications.html Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Fusion http://www.wileyindia.com/biometrics-concepts-and-applications.html Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

contd.. http://www.wileyindia.com/biometrics-concepts-and-applications.html Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Major Challenges Big Data Retrieval Time Awareness Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Parallel Image Processing may help Client Server Communication medium Node-1 Node-2 Node-3 Node-4 Node-5 Node-6 Fractal Image Compressor Parallel Fractal Image Compression Input Image Image De-compressor Decompressed Image Fractal image Decompression Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Soft computing tools help to great extent Computers are used to emulate the reasoning, problem-solving, creativity, and planning behaviors of human beings so that they can solve problems that are too large or too complex to be solved with traditional techniques. The tools are: Fuzzy Sets Neuron Networks Expert Systems (Knowledge-Based Systems) Genetic Algorithms Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD: An Interface between Engineering and Medical Science Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Conventional Medical Diagnosis System Human expertise is a scarce resource whose supply is never guaranteed Human get tired, forget, or simply becomes indolent Humans are inconsistent in their day to day decisions for the same set of data Human can lie, die, and hide Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Screening Challenge high volume small viewing time Complex image interpretation high volume small viewing time Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD + Radiologist Radiologist CAD Radiologist + CAD missed missed oversight Detected Marked Detected missed Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Whereas CAD can diagnose monitor analyze, interpret plan, design, instruct clarify Learn Efficiently Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD combines Creation, recognition, representation, collection, organization, transformation, communication, evaluation and control of information The art, science, engineering and human dimensions Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Why CAD? Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Health Informatics Needed Increasing patient expectation and education Increasing litigation Demand for transparent processes Clinical governance and audit Unmanageable cognitive burden Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Components of Medical Imaging Body Region, Organ, Tissue, Cell Energy sources Detectors Image formation Display User Interface Connection to other Systems Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Anatomy to Physiology Anatomy: Body regions, organs, blood vessels, etc. Physiology: Functions, metabolism, oxygen concentration, blood flow, etc. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Magnetic resonance imaging (MRI) MRI: Non-invasive method used to render images of the inside of an object used to demonstrate pathological or other physiological alterations of living tissues. Pathology: Study and diagnosis of disease through examination of organs, tissues, cells and bodily fluids Physiology: Study of the mechanical and physical functions of living organisms. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

MRI versus CT A computed tomography (CT) also known as computed axial tomography (CAT) uses X-rays as ionizing radiation to acquire images for examining tissue such as bone and calcifications (calcium based) within the body (carbon based flesh), or of structures (vessels, bowel) MRI uses non-ionizing radio frequency (RF) signals to acquire its images and is best suited for non-calcified tissue. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Positron Emission Tomography (PET) PET: Nuclear medicine medical imaging technique which produces a three-dimensional image of functional processes or Metabolic Activities in the body. To conduct the scan, a short-lived radioactive tracer isotope is injected into the living subject (usually into blood circulation). The data set collected in PET is much poorer than CT, so reconstruction techniques are more difficult Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Computer Aided Diagnosis (CAD) CAD has become one of the major support assisting medical experts in diagnosis through images CAD tools are used for measurement, display and analysis of both the structural and functional aspects of the body Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD helps in Visualization: Enhancement for visual analysis Detection: Detect the presence of disease manifestation Localization and Segmentation: Localize or segment the spatial regions containing disease manifestation Other utilities can be used for measurement of various structures from images (length, volume etc. ) Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD in Primary Care Patient visits the doctor with a complaint If required, the patient is then referred by the doctor for specific imaging in order to diagnose the problem The images are analyzed by the experts to arrive at a diagnosis The diagnosis report is used by doctor for planning treatment Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD for Clinical decision making Making sound clinical decisions requires: right information, right time, right format Clinicians face a surplus of information: ambiguous, incomplete, or poorly organized Clinicians are particularly susceptible to errors of omission Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Imaging Process Raw data Reconstruction Filtering “Raw data”                           Processing Signal acquisition Analysis 123…………… 2346………….. 65789………… 6578………….. Quantitative output Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Converting an image into data User extracted qualitative features User extracted quantitative features Examination Level: Feature 1 Feature 2 Feature 3 . Finding: Feature 1 Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD interprets CAD may interpret: Detection of the abnormality Classification: likelihood that the abnormality represents a malignancy Classification, comparison, or analysis of images is performed almost always in terms of a set of features extracted from the images. Usual this is necessary for one of the following reasons: Reduction of dimensionality: an 8-bit per pixel image of size 256x256 pixels has 25665,536 =10157,826 possible realisations. Clearly, it is worth –while to express structure within and similarities between images in ways that depends on fewer, higher-level representations of their pixels and relationship. It will important to show that the reduction nevertheless preserves information important to the task. Incorporation of cues from human perception. Much is known about the effects of basic stimuli on the visual system. In many situations, we have considerable insight into how humans analyse images (essential in the training of radiologist and photo interpreters). Use of the right kinds of features would allow for the incorporation of that experience into automated analysis. Transcend the limit of human perception. Though we can very easily understand many kinds of images, there are properties (e.g. some textures) of images that we cannot perceive visually, but which could be useful in characterising them. Features can be constructed from various manipulations of the images that make those properties evident. Need for invariance. The meaning and the utility of an image are often unchanged when the image is perturbed in various way. Changes in one or more of scale, location, brightness and orientation for example and the presence of noise, artefacts and intrinsic variation are image alteration to which well-designed featured are wholly or partially invariant. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD for Breast Cancer A mammogram is an X-ray of breast tissue used for detection of lumps, changes in breast tissue or calcifications when they're too small to be found in a physical exam. Abnormal tissue shows up a dense white on mammograms. The left scan shows a normal breast while the right one shows malignant calcifications. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Image Analysis in CAD Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Breast Anatomy Breasts consist mainly of fatty tissue interspersed with connective tissue There are also less conspicuous parts lobes ducts lymph nodes Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Micro-calcification and Cancer Micro-calcifications: Tiny deposits of calcium can appear anywhere in a breast and can be seen in a mammogram Most women have one or more areas of micro- calcifications of various sizes Majority of calcium deposits are harmless A small percentage may be precancerous or cancer Some of the cells begin growing abnormally and may spread through the breast, to the lymph or to other parts of the body Common type of breast cancer begins in the milk-production ducts, but cancer may also occur in the lobules or in other breast tissue Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Mammography Uses a low-dose x-ray system to examine breasts Mammography replaces x-ray film by solid- state detectors that convert x-rays into electrical signals which are used to produce images Mammography can show changes in the breast up to two years before a physician can feel Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Detection of Malignant Masses benign malignant Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Difficult Case Heterogeneously dense breast The fibroglandular tissue (white areas) may hide the tumor The breasts of younger women contain more glands and ligaments resulting in dense breast tissue Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Easier Case With age, breast tissue becomes fattier and has less number of glands Cancer is relatively easy to detect in this type of breast tissue Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD Characterization for Lung Cancer Establish the link between computer-based image features of lung nodules in CT scans and visual descriptors defined by human experts (semantic concepts) for automatic interpretation of lung nodules, e.g. Lung nodule has a “solid” texture and has a “sharp” margin Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CAD Characterization of a CT Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Contd.. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Computer-aided characterization Reader 1 Reader 2 Reader 3 Lobulation=1 “marked” Malignancy=5 “highly suspicious” Sphericity=4 Lobulation=4 Malignancy=5 “highly suspicious” Sphericity=2 Lobulation=2 Malignancy=5 “highly suspicious” Sphericity=5 “round” Show how outlines can also be different. Explain that for the same nodule, slices with biggest nodule can be different for different radiologists. Start talking about calculating image features of a nodule, go to the next slide. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Characterization Parameters/Features Characteristic Possible Scores Margin 1. Poorly Defined 2. . 3. . 4. . 5. Sharp Sphericity 1. Linear 2. . 3. Ovoid 4. . 5. Round Spiculation 1. Marked 5. None Subtlety 1. Extremely Subtle 2. Moderately Subtle 3. Fairly Subtle 4. Moderately Obvious 5. Obvious Texture 1. Non-Solid 3. Part Solid/(Mixed) 5. Solid Characteristic Possible Scores Calcification 1. Popcorn 2. Laminated 3. Solid 4. Non-central 5. Central 6. Absent Internal structure 1. Soft Tissue 2. Fluid 3. Fat 4. Air Lobulation 1. Marked 2. . 3. . 4. . 5. None Malignancy 1. Highly Unlikely 2. Moderately Unlikely 3. Indeterminate 4. Moderately Suspicious 5. Highly Suspicious Talk more about interpretation (and interpretation variability) of separate semantic characteristics and move to the next two slides to show a specific example. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Contd.. Shape Features Size Features Intensity Features Texture Features Circularity Area MinIntensity Features calculated from co-occurrence matrices Roughness Convex Area Maxintensity Gabor features Elongation Perimeter SDIntensity Markov Random Field features Compactness Convex Perimeter Contrast   Eccentricity Equiv Diameter Solidity Major Axis Length Minor Axis Length RadialDistanceSD Describe 4 types of features used in a study. Explain how features are mapped to the semantic characteristics. Describe vector representation of a nodule after mapping is done {c1…c7, f1…f64} as input for automatic interpretation algorithm. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Challenges in computer-aided characterization Large number of training samples and features: dimensionality Variation in Nodule size and boundaries Different types of imaging acquisition parameters Clinical evaluation: observer performance studies require collaboration with medical experts or hospitals Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Selection of Features SNR, PSNR, MSE and Entropy Shape Features: Euclidian distance, perimeter, convex perimeter, major and minor axis, rectangularity, convexity, solidity etc. Texture Features: mean, variance, skewness etc. Major axis Minor axis Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Evaluation Parameters True Positive (TP): A case when the suspected abnormality is malignant i.e. the prediction is true. True Negative (TN): If there is no detection of abnormality in healthy person. A case where no symptoms were found truly. False Positives (FP): This is very crucial parameter which indicates that detection of abnormality is found in healthy person. The prediction of presence of abnormality is not true. False Negatives (FN): No detection of malignant lesion is found, proves to be false. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Classification of Tissues Texture Feature Average intensity or gray scale value Average contrast value Smoothness Second moment Uniformity Entropy Tissue types Uncompressed and fatty 43.652 46.314 0.0319 0.451 0.2156 4.876 fatty 68.512 71.236 0.0672 2.451 0.2332 3.253 Non uniform 51.065 81.972 0.0976 8.364 0.5225 4.468 High density 48,173 68.153 0.06472 6.153 0.3273 3.857 Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Image segmentation method Comparative Analysis S. No. Image segmentation method Entropy SNR (dB) 1. Region growing 1.5012 23.65 2. Watershed algorithm 1.4372 31.74 3. k-means clustering 1.5768 28.65 Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Contd… Data base Segmentation methods TP FP FN TN Sensitivity (%) Specificity Accuracy SSGIDB-1 FCM 1468 21 76 121 95.1 85.2 94.2. ANN 1480 19 71 120 95.42 86.43 94.6 GA 1358 23 83 135 94.3 85.5 93.4 SSGIDB-7 1478 65 153 95.8 89 95.2 1519 56 186 96.4 90 95.6 1392 29 69 166 95.27 94.1 SSGIDB-23 37 156 95.3 80.8 93.6 1573 87 146 94.7 88.5 94.2 1492 79 94.9 SSGIDB-39 1479 27 73 152 85 1279 20 162 1378 36 95.5 81 93.81 Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

CBMIR Query Results Image Features Image Database Feature Extraction Similarity Retrieval Image Features Image Database Query Image Query Results Feedback Algorithm User Evaluation Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Features used in CBMIR systems Image features - texture features: statistical and structural shape features Similarity measures -point-based and distribution based metrics Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Performance of CAD Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

Thank You for Kind Attention Any Queries Please!!