# Head-Neck Cancer Delineation
## 4 Other Methods  
In this section different segmentation techniques, which are not based on deep learning, are analyzed. Unlike the section on deep learning techniques, there is no theoretical introduction because each technique is based on different concepts, which are not in common between the various methods. A method with the purpose of segmenting MRI images of patients is described in [PERSON] et al. [43]. It is divided into two parts: image pre-processing and Cancerous lymph nodes 3D segmentation. Different techniques are applied to pre-process the MRI images, in particular for artifacts removing and image enhancing. The noise is removed using morphological operations, which allow us to preserve the edges of the images. Then the images have been enhanced using a background brightness and the values of intensity have been normalized to reduce the intensity variation both intra-slice and inter-slice. Before the segmentation, a process of detection of the lymph nodes is performed. This step is carried by two fuzzy rules for the throat detection and then by a modified fuzzy c-mean (MFCM) which can put the pixels into five clusters, among which there are lymph nodes. The center of the detected lymph nodes is set as a seed for the segmentation carried out by a 3D LSM. The speed function F used for 3D level set evolution is based on the intensity of pixels and on the curvature of the evolving curve. It is computed as follows:  
\[F=\lambda(\epsilon- |I(x,y,z)-T|)+(1-\lambda)\
abla\cdot\frac{\
abla\varphi}{|\
abla \varphi|}\] \[=F_{ext}\ +\ F_{int}\]  
After all, post-processing consists in the use of 3D morphological operations to remove the unsmooth parts, to remove smaller 3D objects and to perform a 3D dilation, to compensate the volume loss in erosion on the bigger one which is the final segmented object. This algorithm is tested on five real datasets of \(\sim\)10 slides each from Beatson West of Scotland Cancer Centre, in Glasgow, obtaining a DSC of 0.9 for the first, \(\sim\)0.8 dataset third and fifth, the second achieved \(\sim\)0.7 and the fourth \(\sim\)0.6. On average, the mean DSC of all the datasets is 0.7. There are also some methods that only use PET images. For example, [PERSON] et al. [44] propose an unsupervised tumor segmentation system for PET images, validated on real PET images of head and neck cancer patients. The first step is the pre-processing, carried out using an anisotropic diffusion filter which automatically removes image noise. In order to segment the PET volume is used a 3D active surface modeling method. This method can detect edges with good accuracy and can be implemented with minimal memory usage. It is implemented using the split Bregman algorithm which aims to minimize the energy function proposed by [PERSON] et al. [45]. After this process, the segmented VOIs are obtained. Then, the values of the pixels of each slide are normalized between 0 and 1 and the slice which contains the maximum intensity value is used as starting point. The slice is compared with the left neighboring slices and if the difference between the maximum values of the compared slices is below a threshold we can move to its closest slide on the left window, otherwise, the propagation is stopped. If the tumor is found only in one or two slices it is labeled as false positive and removed from results. To find the non-detected regions, we performed a morphological dilation and then a 3D maximum bounding box was generated for each dilated VOI, so that can be segmented again using the inner box intensity values. To improve the segmentation accuracy is used the alpha matting method [46]. The testing data is formed by 2 PET images and 2 volumes of a of a custom-built tumour phantom from the XII Turku PET Symposium [47]. The average results in terms of DSC are between 0.5 and 0.7 for the PET images and between 0.5 and 0.65 for the phantom. [PERSON] et al. [48] developed a method known as ATLAAS for the delineation of head and neck cancer in PET/CT images.