Matched Reassignment of the Ultrasound Data of Breast Lesions
(2022) In Bachelor's Theses in Mathematicas Sciences MASK11 20221Mathematical Statistics
- Abstract (Swedish)
- The goal of this thesis was to, from several randomly selected patients with diagnosed malignant and benign tumors, record optimal lambdas and respective Renyi entropies for each lambda, run a basic statistical analysis of the results and see if there is any significant difference between lambdas/Renyi entropies of malignant and benign lesions.\\
The results showed no significant difference.\\
The reassignment technique is a method that moves the signal energy to the center of gravity, giving higher energy concentration at the instantaneous frequency of the signal. The novel matched reassigned spectrogram method (MRS) was recently invented, with the goal to localize and classify transient functions of arbitrary shape. It was tailored for... (More) - The goal of this thesis was to, from several randomly selected patients with diagnosed malignant and benign tumors, record optimal lambdas and respective Renyi entropies for each lambda, run a basic statistical analysis of the results and see if there is any significant difference between lambdas/Renyi entropies of malignant and benign lesions.\\
The results showed no significant difference.\\
The reassignment technique is a method that moves the signal energy to the center of gravity, giving higher energy concentration at the instantaneous frequency of the signal. The novel matched reassigned spectrogram method (MRS) was recently invented, with the goal to localize and classify transient functions of arbitrary shape. It was tailored for very short oscillating transients, which, theoretically, gives perfect reassignment localization to one single point in the time-frequency (TF) plane. Renyi entropy was used as concentration measure, where lambda is its minimizing parameter.
MATLAB algorithm for matched reassigned spectrogram was run on the OASBUD dataset that contains the raw radio-frequency echoes of breast lesions, recorded in two scan planes, along with their respective regions of interest (ROI), and classification of malignancy of the lesion for each patient. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9100030
- author
- Vujasic, Robert LU
- supervisor
- organization
- course
- MASK11 20221
- year
- 2022
- type
- M2 - Bachelor Degree
- subject
- publication/series
- Bachelor's Theses in Mathematicas Sciences
- report number
- LUNFMS-4066-2022
- ISSN
- 1654-6229
- other publication id
- 2022:K10
- language
- English
- id
- 9100030
- date added to LUP
- 2022-09-13 16:02:38
- date last changed
- 2022-09-19 14:39:28
@misc{9100030, abstract = {{The goal of this thesis was to, from several randomly selected patients with diagnosed malignant and benign tumors, record optimal lambdas and respective Renyi entropies for each lambda, run a basic statistical analysis of the results and see if there is any significant difference between lambdas/Renyi entropies of malignant and benign lesions.\\ The results showed no significant difference.\\ The reassignment technique is a method that moves the signal energy to the center of gravity, giving higher energy concentration at the instantaneous frequency of the signal. The novel matched reassigned spectrogram method (MRS) was recently invented, with the goal to localize and classify transient functions of arbitrary shape. It was tailored for very short oscillating transients, which, theoretically, gives perfect reassignment localization to one single point in the time-frequency (TF) plane. Renyi entropy was used as concentration measure, where lambda is its minimizing parameter. MATLAB algorithm for matched reassigned spectrogram was run on the OASBUD dataset that contains the raw radio-frequency echoes of breast lesions, recorded in two scan planes, along with their respective regions of interest (ROI), and classification of malignancy of the lesion for each patient.}}, author = {{Vujasic, Robert}}, issn = {{1654-6229}}, language = {{eng}}, note = {{Student Paper}}, series = {{Bachelor's Theses in Mathematicas Sciences}}, title = {{Matched Reassignment of the Ultrasound Data of Breast Lesions}}, year = {{2022}}, }