1. REU student: Winona Richey Graduate student: Naji Khosravan
Automatic Lung Nodule Detection Using Deep Learning Week 2: Literature Review
REU student: Winona Richey
Graduate student: Naji Khosravan
Professor: Dr. Bagci

2. Incorporating Hand Crafted Features
Neural Net Nodule CAD systems are great
Easily recognize local features
Shape, Border, Volume, Density
Can identify patterns and trends between nodules
Even trends not obvious to the human eye
No fatigue (unlike humans)
high quantities  same accuracy
Global/High Level Features are also very important
3 Dimensional spatial relationships
Textural pattern relationships/ Geography of a pattern
Hand crafted features vs. CNN
Can more easily identify some features (faster)
Characterize different features
Combination leads to more complete information
Reduce dependency on CNN for a lighter model  time efficient
Convolutional neural networks are great, as we’re all aware. They can easily recognize local features, patterns and trends between nodules and they don’t get fatigued the way humans do. But some important characteristics can also be identified with handcrafted feature detection. Combining handcrafted features with a convolutional neural network will allow us to more completely characterize nodules and even make a lighter CNN model to increase time efficiency.
Image: Ciompi, et al Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images.

3. Previous Prevalence of Hand Crafted Features
Many features can be detected using hand crafted features
Shape
Density
Border
Volume
Texture
Patterns A B C
Coronal views from CT scans of
Nonspiculated nodules
Vascular structures
Spiculated nodules
Image: Ciompi, et al Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images.

4. State of the Art Hand Crafted Features
Bag of Frequencies
Characterize nodules based on morphology
Feature vectors from frequency based spectral signatures
Taxonomic Diversity/Distinctness Indexes
Characterize nodules based on texture and patterns
Analyzing intensity distribution/relationships
Utilizing phylogenetic trees (from ecology)
Spherical Harmonics
Boundary description
Shape representation of object surfaces
… and many more
Some state of the art hand crafted features for use in lung nodule detection and classification are taxonomic indices, bag of frequencies and spherical harmonics. The bag of frequencies technique characterizes nodules based on morphology using intensities. T.I.s is a method for characterizing nodules based on texture and patterns by analyzing the intensity distribution.
a. Nodule
b. Non-Nodule (blood vessel)
Image: de Carvalho Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM.

5. References
Ciompi, F., Jacobs, C., Scholten, E. T., Wille, M. W., de Jong, P. A., Prokop, M., & van Ginneken, B. (2015). Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images. IEEE Transactions On Medical Imaging, 34(4),
de Carvalho Filho, A. O., Silva, A. C., de Paiva, A. C., Nunes, R. A., & Gattass, M. (2016). Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM. Journal of Signal Processing Systems, 1-18.
Gerig, G., Styner, M., Jones, D., Weinberger, D., & Lieberman, J. (2001). Shape analysis of brain ventricles using spharm. In Mathematical Methods in Biomedical Image Analysis, MMBIA IEEE Workshop on (pp ). IEEE.