Francesco TORTORELLA | MEDICAL IMAGING
Francesco TORTORELLA MEDICAL IMAGING
cod. 0623200007
MEDICAL IMAGING
| 0623200007 | |
| DEPARTMENT OF INFORMATION AND ELECTRICAL ENGINEERING AND APPLIED MATHEMATICS | |
| EQF7 | |
| INFORMATION ENGINEERING FOR DIGITAL MEDICINE | |
| 2024/2025 |
| OBBLIGATORIO | |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2022 | |
| AUTUMN SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/05 | 4 | 32 | LESSONS | |
| ING-INF/05 | 2 | 16 | EXERCISES |
| Objectives | |
|---|---|
| THE COURSE PROVIDES THEORETICAL AND TECHNOLOGICAL KNOWLEDGE ABOUT THE MAIN TYPES OF MEDICAL IMAGES AND ON THE METHODOLOGIES FOR PROCESSING SUCH IMAGES, IMPROVING THEIR QUALITY AND EXTRACTING INFORMATION RELEVANT FOR THEIR DIAGNOSIS. KNOWLEDGE AND UNDERSTANDING PHASES OF AN IMAGE PROCESSING SYSTEM, WITH PARTICULAR FOCUS ON THE PHASES OF LOW LEVEL PROCESSING (ACQUISITION, FILTERING), INTERMEDIATE LEVEL PROCESSING (EXTRACTION OF REGIONS AND CONTOURS) AND HIGH LEVEL PROCESSING (SHAPE RECOGNITION), TOGETHER WITH THE BASIC TECHNIQUES FOR IMPLEMENTING THESE FUNCTIONS WITH EXISTING LIBRARIES AND FRAMEWORKS; CHARACTERISTICS OF THE TYPES OF IMAGES USED IN THE MEDICAL FIELD (RADIOGRAPHIC IMAGES, CT, PET, MRI AND ULTRASOUND IMAGES) AND THEIR REPRESENTATION; MAIN FRAMEWORKS FOR IMAGE ANALYSIS. APPLYING KNOWLEDGE AND UNDERSTANDING DESIGNING AND IMPLEMENTING APPLICATIONS BASED ON THE ANALYSIS AND INTERPRETATION OF MEDICAL IMAGES, USING DEDICATED FRAMEWORKS AND LIBRARIES. |
| Prerequisites | |
|---|---|
| PROPAEDEUTIC COURSE: MACHINE LEARNING KNOWLEDGE OF A PROGRAMMING LANGUAGE SUCH AS PYHTON OR C IS ASSUMED |
| Contents | |
|---|---|
| Teaching unit 1: DIGITAL MEDICAL IMAGES: ACQUISITION AND STANDARD FORMATS (LECTURE/PRACTICE/LABORATORY Hours 12/4/0) - 1 (2 Hours Lecture): Origin of digital images. Spectrum of electromagnetic radiation. Color spaces - 2 (2 Hours Lecture): Human perception and mechanism of vision. Spatial sampling. Sensors. - 3 (2 Hours Lecture): X-ray images - 4 (2 Hours Practice): Simple operations on digital images - 5 (2 Hours Lecture): CT scan: principles and images. - 6 (2 Hours Lecture): MRI: principles and images - 7 (2 Hours Lecture): Image formats. DICOM - 8 (2 Hours Practice): DICOM image management KNOWLEDGE AND UNDERSTANDING: Concept of digital medical image. Principles underlying the most widespread image modalities. Characteristics and organization of the DICOM format APPLIED KNOWLEDGE AND UNDERSTANDING: Manage the DICOM files of medical images and retrieve the main information within it. Recognize the main features and modalities of a medical image. Didactic unit 2: POINT AND LOCAL OPERATIONS. (LECTURE/PRACTICE/LABORATORY Hours 8/4/0) - 9 (2 Hours Lecture): Operations on digital images (punctual, local, global). Image histogram. Transformations for contrast enhancement - 10 (2 Hours Lecture): Histogram equalization. Adaptive equalization - 11 (2 Hours Exercise): Implementation and use of transformations for contrast enhancemente and equalization - 12 (2 Hours Lecture): Two-dimensional filters: characteristics and properties. Noise Removal Filters. Bilateral filters. - 13 (2 Hours Lecture): Derivative filters. Laplacian. Sharpening - 14 (2 Hours Practice): Implementation and application of filtering to medical images KNOWLEDGE AND UNDERSTANDING: Characteristics and properties of the main transformation and filters for medical images APPLIED KNOWLEDGE AND UNDERSTANDING: Apply point and local operations to improve the characteristics of medical images Didactic unit 3: MATHEMATICAL MORPHOLOGY (LECTURE/PRACTICE/LABORATORY Hours 6/2/0) - 15 (2 Hours Lecture): Basic morphological operations on binary and gray level images - 16 (2 Hours Lecture): Transform top hat. Morphological reconstruction - 17 (2 Hours Lecture): Watershed transformation - 18 (2 Hours Practice): Application of morphological operations to medical images KNOWLEDGE AND UNDERSTANDING: Characteristics and properties of morphological operations APPLIED KNOWLEDGE AND UNDERSTANDING: Apply morphological operations for the analysis and interpretation of medical images Didactic unit 4: TECHNIQUES BASED ON MACHINE LEARNING (LECTURE/PRACTICE/LABORATORY Hours 6/6/0) - 19 (2 Hours Lecture): Machine Learning for the analysis of medical images. The ROC curve. Radiomics. - 20 (2 Hours Lecture): Applications based on radiomics - 21 (2 Hours Exercise): Implementation and use of radiomics for the analysis of medical images - 22 (2 Hours Lecture): Deep learning based models for medical image analysis - 23 (2 Hours Exercise): Implementation and use of deep networks for the analysis of medical images - 24 (2 Hours Exercise): Implementation and use of deep networks for the analysis of medical images KNOWLEDGE AND UNDERSTANDING: How to use Machine Learning for the analysis and interpretation of medical images APPLIED KNOWLEDGE AND UNDERSTANDING: Design and implement medical image analysis techniques based on machine learning algorithms TOTAL LECTURE/PRACTICE/LABORATORY Hours 32/16/0 |
| Teaching Methods | |
|---|---|
| TEACHING ACTIVITIES INCLUDE THEORETICAL LESSONS AND EXERCISES. STUDENTS WILL BE ASSIGNED BOTH INDIVIDUAL AND GROUP PROJECTS, IN WHICH THEY WILL USE METHODOLOGIES AND DEVELOPMENT TOOLS PRESENTED IN THE COURSE. IN ORDER TO BE ABLE TO SUPPORT THE FINAL PROFIT VERIFICATION AND ACHIEVE THE CFU RELATED TO THE TRAINING ACTIVITY, THE STUDENT MUST HAVE ATTENDED AT LEAST 70% OF THE HOURS PROVIDED FOR ASSISTED EDUCATIONAL ACTIVITIES. |
| Verification of learning | |
|---|---|
| THE EXAM IS AIMED AT EVALUATING IF THE STUDENT MASTERS THE TOPICS PRESENTED IN THE COURSE AND IF SHE/HE IS ABLE TO APPLY THE ACQUIRED KNOWLEDGE TO THE RESOLUTION OF REAL PROBLEMS. THE EXAM COULD REQUIRE THE REALIZATION OF A PROJECT WHOSE CONTENT IS PREVIOUSLY AGREED WITH THE TEACHER AND IS RELATED TO ONE OF THE TOPICS PRESENTED IN THE COURSE: THE DESIGN AND METHODOLOGICAL CHOICES MADE ARE CONSIDERED FOR THE ASSESSMENT. |
| Texts | |
|---|---|
| R.C. GONZALEZ, R.E. WOODS, DIGITAL IMAGE PROCESSING, 4TH ED., PEARSON COLLEGE DIV., 2017 A. WEBB, INTRODUCTION TO BIOMEDICAL IMAGING, IEEE PRESS, 2004 SUPPLEMENTARY TEACHING MATERIAL WILL BE AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS. |
| More Information | |
|---|---|
| THE COURSE IS HELD IN ENGLISH |
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2024-11-18]
