Added details in readme

README.md

@@ -1,8 +1,14 @@
-# eReg
+# eReg - A Simple Registration Tool
+
+## Need
+
+Because of security concerns, users in clinical environments do not have access to virtualization and containerization technologies such as Docker and Singularity. This becomes a problem, because most research code (especially for image registration) is built around the need to have access to these technologies. Alternatively, some tools only work on a Linux environment, or they need specific hardware resources (such as a DL accelerator card), which are not always available in clinical settings.
+
+**eReg** is a simple registration tool that can be used in clinical environments without the need for virtualization or containerization technologies. It supports most platforms across various hardware configurations.
 
 ## Installation
 
-With a Python 3.8+ environment, you can install eReg from [pypi.org](https://pypi.org/project/eReg/).
+With a Python 3.8+ environment, you can install **eReg** from [pypi.org](https://pypi.org/project/eReg/).
 
 1. Create a virtual environment
 
@@ -24,19 +30,9 @@ source venv_ereg/bin/activate ## using native python venv
 pip install ereg
 ```
 
-## Extending eReg
-
-To extend eReg, you first need to install eReg from source. Clone the repository and install the package:
-
-```sh
-git clone https://github.com/BrainLesion/eReg.git
-cd eReg
-pip install -e .
-```
-
 ## Usage
 
-eReg can be used via the command line or as a Python package. 
+**eReg** can be used via the command line or as a Python package. 
 
 ### Command Line Interface
 
@@ -60,37 +56,68 @@ options:
 ```
 
 ### Pythonic Interface
+The `ereg` package provides two Python interfaces, an object-oriented interface, as well as convenience functions.
 
-The Pythonic interface is available via the `ereg` package, and can be used in two ways: as a functional interface or as an object-oriented interface.
+#### Object-Oriented Interface
 
-#### Functional Interface
+The `register` method represents the core-of the object-oriented interface:
 
 ```python
-from ereg import registration_function
+from ereg.registration import RegistrationClass
 
-ssim = registration_function(
+registration_obj = RegistrationClass(configuration_file) # the configuration file to use to customize the registration, and is optional
+registration_obj.register(
     target_image=target_image_file, # the target image, which can be either a file or SimpleITK.Image object
     moving_image=moving_image_file, # the moving image, which can be either a file or SimpleITK.Image object
     output_image=output_file, # the output image to save the registered image to
     transform_file=transform_file, # the transform file to save the transform to; if already present, will use this transform instead of computing a new one
     log_file=log_file, # the log file to write to
-    configuration=configuration_file, # the configuration file to use to customize the registration, and is optional
 )
 ```
 
-#### Object-Oriented Interface
+Further, a resample method is available to use previously computed transforms to resample a moving image:
 
 ```python
-from ereg.registration import RegistrationClass
+registration_obj.resample_image(
+    target_image=target_image_file,
+    moving_image=moving_image_file,
+    output_image=output_file,
+    transform_file=transform_file,
+    log_file=log_file,
+)
+```
 
-registration_obj = RegistrationClass(configuration_file) # the configuration file to use to customize the registration, and is optional
-registration_obj.register(
+
+#### Functional Interface
+
+Additionally, **eReg** provides functional wrappers for convenience.
+
+```python
+from ereg import registration_function
+
+ssim = registration_function(
     target_image=target_image_file, # the target image, which can be either a file or SimpleITK.Image object
     moving_image=moving_image_file, # the moving image, which can be either a file or SimpleITK.Image object
     output_image=output_file, # the output image to save the registered image to
     transform_file=transform_file, # the transform file to save the transform to; if already present, will use this transform instead of computing a new one
     log_file=log_file, # the log file to write to
+    configuration=configuration_file, # the configuration file to use to customize the registration, and is optional
 )
 ```
 
-## TODO
+## Customization
+
+eReg's registration and transformation parameters can be customized using a configuration file. The configuration file is a YAML file that contains the parameters for the registration. The default configuration file is present [here](https://github.com/BrainLesion/eReg/blob/main/ereg/configurations/sample_config.yaml). More details on the parameters and their options can be found in the configuration file itself.
+
+
+## Extending eReg
+
+To extend eReg, you first need to install **eReg** from source. Clone the repository and install the package:
+
+```sh
+git clone https://github.com/BrainLesion/eReg.git
+cd eReg
+pip install -e .
+```
+
+<!-- ## Citation TODO -->

ereg/registration.py

@@ -220,7 +220,7 @@ def resample_image(
         transform_file: str = None,
         log_file: str = None,
         **kwargs,
-    ) -> None:
+    ) -> float:
         """
         Resample the moving image to the target image.
 
@@ -269,6 +269,8 @@ def resample_image(
                 )
                 logging.shutdown()
 
+                return self.ssim_score
+
     def _get_transform_wrapper(self, transform: str, dim: int) -> sitk.Transform:
         """
         Get the transform class.