Methods Inf Med 2016; 55(03): 215-222
DOI: 10.3414/ME15-01-0148
Original Articles
Schattauer GmbH

Evaluation of Spontaneous Spinal Cerebrospinal Fluid Leaks Disease by Computerized Image Processing

Mustafa S. Yıldırım
1   Fatih University, Institute of Biomedical Engineering, Istanbul, Turkey
,
Sadık Kara
1   Fatih University, Institute of Biomedical Engineering, Istanbul, Turkey
,
Mehmet S. Albayram
2   Istanbul University, Faculty of Medicine, Istanbul, Turkey
,
Şükrü Okkesim
1   Fatih University, Institute of Biomedical Engineering, Istanbul, Turkey
› Author Affiliations
Further Information

Publication History

received: 09 November 2015

accepted in revised form: 21 March 2016

Publication Date:
08 January 2018 (online)

Summary

Background: Spontaneous Spinal Cerebro -spinal Fluid Leaks (SSCFL) is a disease based on tears on the dura mater. Due to widespread symptoms and low frequency of the disease, diagnosis is problematic. Diagnostic lumbar puncture is commonly used for diagnosing SSCFL, though it is invasive and may cause pain, inflammation or new leakages. T2-weighted MR imaging is also used for diagnosis; however, the literature on T2-weighted MRI states that findings for diagnosis of SSCFL could be erroneous when differentiating the diseased and control. One another technique for diagnosis is CT-myelography, but this has been suggested to be less successful than T2-weighted MRI and it needs an initial lumbar puncture.

Objectives: This study aimed to develop an objective, computerized numerical analysis method using noninvasive routine Magnetic Resonance Images that can be used in the evaluation and diagnosis of SSCFL disease.

Methods: Brain boundaries were automatically detected using methods of mathematical morphology, and a distance transform was employed. According to normalized distances, average densities of certain sites were proportioned and a numerical criterion related to cerebrospinal fluid distribution was calculated.

Results: The developed method was able to differentiate between 14 patients and 14 control subjects significantly with p = 0.0088 and d = 0.958. Also, the pre and post-treatment MRI of four patients was obtained and analyzed. The results were differentiated statistically (p = 0.0320, d = 0.853).

Conclusions: An original, noninvasive and objective diagnostic test based on computerized image processing has been developed for evaluation of SSCFL. To our knowledge, this is the first computerized image processing method for evaluation of the disease. Discrimination between patients and controls shows the validity of the method. Also, post-treatment changes observed in four patients support this verdict.

 
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