# A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification
## 2 Materials and Methods
### Biological versus Computational Features  
As mentioned before, during the screening examination in a cytology laboratory, the cytopathologist manually analyzes optical images of cervical cells. Visual analysis is related to human interpretation of cervical smears, and even with many detailed procedures and routines, it is susceptible to errors of interpretation. During the analysis, the cytopathologist assesses the variation in the smear cells' cytomorphological features. Some examples of this variation are the increase in the nucleus/cytoplasm ratio, the nuclear membrane irregularity, the nucleus hyperchromasia, and the chromatin granularity. All of them provide guidance on reporting of cytologic findings in cervical cytology in agreement with the Bethesda System [4,34]. Errors related to diagnostic interpretation happen when the cytopathologist either recognizes altered cells, but wrongly classifies them, or does not recognize them at all. Both situations may be attributed to the lack of experience of the professional, variation in the appearance of cytomorphological features, or workload, which affects the subjectivity of the process [35, 36, 37]. Our proposal extracts and evaluates morphological and texture characteristics related to the cell nucleus, correlated to the Bethesda System's visual interpretation. The computational results can guide the cell classification systems and assist the lesion diagnosis and interpretation, diminishing error results. The methodology starts with a feature extraction of each nucleus segmented in the database. The following algorithms were employed: Region Props, Haralick's features, Local Binary Patterns (LBP), Threshold Adjacency Statistics (TAS), Zernike moments, and Gray Level Co-occurrence Matrix (GLCM). All were implemented in Python, in which Region Props and GLCM are from the scikit-image package [38], and the others are from the Mahotas package [39]. Unlike [PERSON] et al. [32], we also include morphological and other texture features. First, Region Props [40, 41, 42, 43] was used to extract the values of the morphological features of nuclei, such as (a) circularity; (b) minimum, mean, and maximum intensities; (c) area; (d) bounding box, filled, and convex hull image areas; (e) perimeter; (f) Euler number; (g) extent; (h) minor and major axis; (i) eccentricity; and (j) solidity. Next, [PERSON]'s texture features [44] were extracted. These features are: (a) angular second moment; (b) contrast; (c) correlation; (d) variance; (e) inverse difference moment; (f) sum average; (g) sum entropy; (h) entropy; (i) difference variance; (j) difference entropy; (k) measure of correlation 1; (l) measure of correlation 2. \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Database** & **Category** & **Classification** & **Quantity** \\ \hline \multirow{8}{*}{Herlev} & \multirow{2}{*}{Normal} & Superficial squamous epithelial & 74 \\ \cline{3-4}  & & Intermediate squamous epithelial & 70 \\ \cline{3-4}  & & Columnar epithelial & 98 \\ \cline{3-4}  & & Mild squamous non-keratinizing dysplasia & 182 \\ \cline{3-4}  & \multirow{2}{*}{Abnormal} & Moderate squamous non-keratinizing dysplasia & 146 \\ \cline{3-4}  & & Severe squamous non-keratinizing dysplasia & 197 \\ \cline{3-4}  & & Squamous cell carcinoma in situ intermediate & 150 \\ \hline \multirow{8}{*}{CRIC} & Normal & Negative for intraepithelial lesion or malignancy (NILM) & 862 \\ \cline{3-4}  & & Atypical squamous cells of undetermined significance (ASC-US) & 286 \\ \cline{3-4}  & & Low-grade squamous intraepithelial lesion (LSIL) & 598 \\ \cline{3-4}  & & Atypical squamous cells cannot exclude HSIL (ASC-H) & 536 \\ \cline{3-4}  & & High-grade squamous intraepithelial lesion (HSIL) & 874 \\ \cline{3-4}  & & Squamous carcinoma (SC) & 77 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Database division. The Local Binary Patterns (LBP) [45], a set of texture features, were also extracted. The advantage of these features is that they are insensitive to orientation and lighting.