Medical Pattern Understanding and Cognitive Analysis in .NET

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Medical Pattern Understanding and Cognitive Analysis
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Table 14.4 Production set SP defining pathological changes in pancreatic ducts. Lesion Cyst Grammar rules 1. LESION CYST 2. CYST I P NI G P NG I P NG G P NI I S NI G S NG I S NG G S NI I NS NI G NS NG I NS NG G NS NI 3. LESION STENOSIS 4. STENOSIS NS S NS G NS P S NS P I NG S NI NS I NI S 5. LESION DILATATION 6. DILATATION S P NG S G NS S NS G NS 7. LESION BRANCH 8. BRANCH I NI I NS I P NI NN I NS NI NN G NI G S NN G P NN G S NI NN S NG S NS NN N G NG NI 9. N n N n 10. NN nn NN nn 11. I i I i 12. NI ni NI ni 13. G g G g 14. NG ng NG ng 15. S s S s 16. NS ns NS ns 17. P p Semantic actions Lesion = Cyst
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Lesion = Stenosis
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Lesion = Dilatation
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Lesion = Branch
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to define their morphology precisely, but based only on transitions between the so-called sinquads (that is, specified directions of the sectors approximating the width diagrams). This analysis is more universal in its character than an analysis using grammars, due to the fact that it allows identification of all types of convexity, ramification and stenosis even in situations in which those lesions are not recognized by the previously described context-free grammar. Despite the complicated grammatical reasoning process, in this case, the syntactic methods of image recognition also supply practically all information on morphological lesions on the external edges of the pancreatic ducts analyzed here. As for the grammars analyzing coronary arteries and ureters, for the sequential grammar presented in this section, a syntax analyzer has been constructed in the form of a parser generated with the use of the YACC compiler. This analyzer was subject to a number of experiments; as a result it obtained very good results in recognizing the looked-for lesions on ERCP images. In the case of analysis of those images, for the shape feature analysis procedure, a sequential transducer was also implemented, a counterpart of the syntax analyzer for languages of description of shape features. The efficacy of this method of computer-aided diagnosis of cancerous and inflammatory lesions based on ERCP pictures can be estimated at 90 %. This value stands for the percentage of correct recognitions of symptoms defined in the grammar: symptoms in the form of strictures, ramifications and lesions with cyst-like features. The remaining 10 % of the analyzed pancreatic images, which were
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Semantic Analysis of Spinal Cord NMR Images
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not fully correctly interpreted, can be considered to be cases difficult to interpret. Such images can present, among others, incorrect ramifications looping on the main image with the pancreatic duct, or show amputation (discontinuation) of the duct. Recognition of these may require either 3D analysis or independent interpretation of separated duct parts. Some images, as in the case of artery and ureter images, should be analyzed with a dynamically selected approximation threshold. These cases are also included in the group of images that are difficult to interpret. Figure 14.5 presents examples of recognition of looked-for lesions in ERCP images.
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7. Semantic Analysis of Spinal Cord NMR Images
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Another interesting application of the syntactic approach to automatic perception analysis involves the morphological classification of diseases of the spinal cord and surrounding spinal meninges [35]. This section provides an example of the analysis of such structures based on the scrutiny of congenital and developmental anomalies in the cord structure, well apparent in Magnetic Resonance Images (MRI). There is a great variety of central nervous system and spinal cord diseases of variable etiology, including congenital and developmental anomalies revealed as lesions of spinal cord sections, or as dysfunctions of the nervous system. Apart from most common spinal cord diseases, the list includes cerebrospinal meningitis caused by bacteria, viruses or pathogenic parasites entering the central nervous system, polioencephalitis or poliomyelitis, inflammatory conditions in the whole cord width either diffused, disseminated or focused. These serious cases might be subjected to semantic analysis using structural classifiers. Diffuse diseases of the spinal cord may be caused by lesions in anterior and posterior spinal arteries. The occlusion of blood vessels might cause significant deterioration of the patient s condition, leading to a paralysis of sometimes even all limbs. Spinal cord damage caused by vascular problems might originate from the spinal cord tissue, sensory roots of a spinal nerve, meninges, a vascular rete in the spinal cord, the vertebrae or organs in the direct vicinity of disks. There might also be metastases from other organs. In the case of analysis of backbone and spinal cord MRI images, the chief objective in the recognition stage is to detect and diagnose lesions that might evidence a whole range of various disease units: from myelomeningocele, numerous forms of inflammatory condition or cerebral or spinal cord ischemia, to the most serious cases of intra- and extramedullary tumors. An unambiguous identification of all units with the use of one recognizing software is extremely difficult, due to rather subtle differences that are decisive for the correct classification of every one of them. All the same, structural analysis proves to be extremely useful in the specification of the degree of the disease unit development by means of specifying the size of lesions in the morphology of the cord and by defining the compression of the spinal cord and meninges [35]. The analysis of this structure uses a developed context-free grammar. It allows us to identify the symptoms and to draw diagnostic conclusions relating to the inner nature of the visible pathology. The grammar developed for the analysis of spinal cord images is defined as follows: Gsc = VN VT STS SP , where: VN = LESION, NARROWING, ENLARGEMENT, H, E, N VT = h, e, n for h 11 11 e 11 180 n 11 180 STS = LESION SP is defined in Table 14.5. This grammar permits one to detect different forms of narrowing and enlargement which may characterize the different disease units (for example neoplasm or inflammation processes). The results of spinal cord morphology analyses aimed at locating lesions are provided in Figure 14.6.
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